35664, We remove the highly correlated features to avoid the problem of multicollinearity explained earlier 13485, Check versions 31347, Implementing Stacking 6731, OverallQual Vs SalePrice 43197, Ensemble two model NN LGBM 8278, Preparing Data for Modelling 36469, Images from ROLLS ROYCE ENGINEERING SPECIFICATION INDEX 12805, Load the Data 7583, For all SF we further add some of the outside area values 15367, Title feature 39187, LARCENY THEFT a categoria de crimes com maior n mero de ocorr ncias Onde estas ocorr ncias mais se concentram 7998, Train SGD Regressor 41868, XGBoost baseline model 27334, Label plot 20171, Out of parameters below we be playing with Gamma and C where 13085, Random Forest Classifier 3823, First we graph the population distribution of survivors for reference 497, We extract first letter of assigned cabin and then map it into a category 1305, Observations 35591, Transfer Learning 10598, Step 2 Know Your Data 27974, countplot 29440, The list of common places include strings like earth or Everywhere It s because this field is the users input and were not automatically generated and is very dirty 11209, Use another Stacking function from mlxtend 142, Decision Tree Classifier 26056, Examining the Model 36787, Ambiguity 18473, CompetitionDistance 34975, Library 18028, Perform the gridsearch 12753, Inspired by such features we can add another FamilySize column and IsAlone column 26864, The box filter aims at replacing the old pixel value by the mean of all the pixel of its neighbourhood 32983, Polynomial regression 24267, Observations from the Pearson analysis 9956, Random Forest 13659, Fare 4784, we can zoom in to the 10 most important variables according to the pearson correlation coefficient and check the matrix 13669, SVM 12375, Box Plot of the sale price over the whole dataset 22284, GridSearchCV and RandomizedSearchCV are excellent tools for determing the best hyperparameters for your models This can increase your model accuracy significantly 26853, Model Training using Vowpal Wabbit Algorithm 4047, We can drop out the Passenger ID column 42231, Numerical columns within the dataset 16517, Lets use pycaret 24273, Perceptron 3447, check that Age Title boxplot again 8337, Surprisingly only two features are dominant OverallQual and TotalSF 22749, Crimes by year month 12069, TotalBsmtSF 34507, Our param grid is set up as a dictionary so that GridSearch can take in and read the parameters 23720, Storing best hyper parameters for XGB Regresssor into bestP DataFrame 35641, Kindly upvote the kernel if you like it font 18352, SCALING VARIABLES 39123, With Random Forest 1717, Import Datasets 41592, User Features 9590, Finding missing values 21657, Using glob to generate a df from multiple files duplicated Trick 78 10972, Linear regression L1 regularisation 12570, that we are ready with the pre processed data we start feeding the training data into machine learning models and make predictions on the test data 10211, First start with passenger s Age 31891, logistic Regression 7109, We use five models to apply votingclassifier namely logistic regression random forest gradient boosting decision support vector machine and k nearest neighbors 4572, We ll check again if we have filled all missing values 1934, Building remodelling years and age of house 11657, K Nearest Neigbors 18582, It s important that you have a working NVidia GPU set up 6738, LotFrontage Vs SalePrice 18236, This is a 5 layers Sequential Convolutional Neural Network for digits recognition trained on MNIST dataset 27658, In order to avoid overfitting problem we need to expand artificially our handwritten digit dataset 14253, Sibling brother sister stepbrother stepsister 28592, Fireplaces 29986, We ll use as the HDF5 interface and multiprocessing for image pre processing 15662, xgboost 28731, Category s by items sold by day Crosstab 4036, Correlation with SalePrice 30763, Save data 24954, Embarked in Train set 35914, Callback 34656, Price in USD 33297, Housekeeping 11189, Elastic Net Regression 4280, Exploratory Data Analysis 25677, Text Analysis Character and Word counts 13794, Multi layer Perceptron 7236, Finding the optimum Alpha value using cross validation 23216, we need a black box function to use baian optimization 16431, Random Forest 15114, as our last step before modeling we separate the combined dataset into its train and test versions once again 14512, Observations 3980, Describe the data 14258, Fare Range 26426, Implement new feature AgeCat 21795, Quadratic Discriminant Analysis 7034, Fence quality 26594, TASK 5 TRAIN THE MODEL PART A 2332, Take Your Position 2536, In this small part we isolate the outliers with an IsolationForest 20721, Neighborhood column 2822, Splite the data to training set and a validation set 28630, GarageYrBlt 18779, PoolQC data description says NA means No Pool That make sense given the huge ratio of missing value 99 and majority of houses have no Pool at all in general 2436, We can also take a look directly at what the most important coefficients are 20475, City registered not live city and not work city 13415, Cross Validation 15747, Cleaning Data 27000, Checking the improvement 14732, Evaluating the Model 14748, Evaluating Our RBF Model 42830, While the sample mean differences are more or less balanced around zero the sample variance differences are almost entirely on the negative side 16909, fillna Embarked 37832, Naive Bayes 6375, Find out the median value 15314, Lets assign X value to all the NAN values 15247, Feature importance 26517, Cod prov Province Code distribution 28807, Prophet 14322, Sibsp 5319, we ll try to remove features with lowest variance 30079, y hat consists of class probabilities 3065, Initial 37991, 1Remarkable after the fist epoch 40795, Categorical Features 22751, Function to Model SIR Framework 12158, Criterion 22138, GarageArea Size of garage in square feet 43126, Random forest 16842, Now there is only one missing value inside the Fare column in test data 721, drop these columns 39149, Load data into DataBunch 25864, Linear Model Prediction on test set 4328, Minors children age 18 3421, Families or groups traveling together may have had similar prefix codes for example 10173, Can also be used to analyze null values 15347, In case you wanna take a look at the data 20733, TotalBsmtSF column 15307, Training Decision Tree Model Again 34011, No outliers 19750, How good does our process work 24520, There are some customers with seniority less than zero 14201, do some plots 16942, The feature importance is not exactly the same but it is close 30140, Callbacks Optional 25083, How aout HOBBIES 31869, Class Balance on fly with XLA 34332, Preprocessing test data 40393, Define Model 11963, We now create a function that would predict each value depending on the weights set by us We can tune these weights however we want to get the least error possible 38685, Probability of melanoma with respect to Age 13419, Ensemble Modeling 42744, The clients with short overdue days is most numerous 28000, Needed Libraries 21346, Understanding the Model Better 30889, we compute predictions on the test data set and save them 22426, Understanding the figure subplots and axes in matplotlib 40692, note that the highest demand is in hours from say and the from this is bcoz in most of the metroploitan cities this is the peak office time and so more people would be renting bikes this is just one of the plausible reason 4466, Checking for missing values in dataset 6093, Outliers can affect a regression model by pulling our estimated regression line further away from the true population regression line 30326, For neutral samples use original texts as they are 4821, Log transformation is just a special case of Box Cox transformation let us apply it on our prices data to make it more normal 28156, import the BERT tokenizer used to convert our text into tokens that correspond to BERT s vocabulary 14307, Analyze the Correlation between Features 10236, With this we are ready to get predicted values and submission file 10608, MachineLearning 102 Intermediate Working on Feature Set 36533, we now generate a list of pairs from each of the class 35762, reference learn org stable auto examples model selection plot underfitting overfitting htmlsphx glr auto examples model selection plot underfitting overfitting py 25417, Some plots 18063, Evaluate Model Function 3580, And treats the missing values as None like BsmtCond variable 28058, Categorical variables 27158, GarageType Garage location 21564, FamilySize 29186, Finalizing X and scaling train test separately 4137, Basic PyTorch NN model for Regression Analysis 36087, Inference 41363, Pool data is not so useful for us 29861, data element function 21838, One hot Encode Categorical Variables 23976, Model 39880, Drop Id and SalePrice column 27051, Malignant category 12434, Filling all null categorical and numerical features e features that are almost constant 42860, Train the model 20776, T SNE applied on Doc2Vec embedding 29470, Univariate analysis of feature word common 1779, ROC Curve 12745, let s deal with Fare and Embark 12937, Fare Column 12831, Some insight in Fare information 40178, Code from notebook 17561, Check Missing Values 34626, ConvNets 275, Prediction 22787, Barplot 24034, It is also interesting how price affect sales 11211, Check predictions are they one same scale as SalePrice in Train dataset 10546, Random Forest Regressor 6669, Random Forest Classification 4606, Other Categorical Features 7006, Wood deck area in square feet 1518, Still we have a lot of features to analyse here so let s take the strongly correlated quantitative features from this dataset and analyse them one by one 20353, Our plot of train and validation loss looks good 41000, Basic Block 36779, Test Data 11278, Looking at the Name column There is a title like Mr or Mrs within each as well as some less common titles like the Countess 24515, This is much better than the severely imbalanced case but still not as good as training on the original dataset 34349, The embedding sizes we are going to use for each category 10599, Lets find out more about Training Set 11450, We use the Name feature to extract the Titles from the Name so that we can build a new feature out of that 15582, Gaussian Naive Bayes 27154, BsmtQual Evaluates the height of the basement 25082, compare the average daily sales per event 20806, SalePrice Distribution Visualization 26532, Submit 29544, we ll get Classification Report and Confusion Matrix 38041, check out the Probability of picking a house in the Neighborhood OldTown 28286, prepare target dataset too 37434, Check for bigrams in selected text font 15997, Searching the best params for Random Forest 23991, For missing values in numerical cols we fillNa with 27067, Count keyword 41266, Alternatively you can use plt 22869, Classification Fully Connected Layer FC Layer 29716, Scatter plot 4168, Square root transformation 34876, now we take care of common misspellings when using american british vocab and replacing a few modern words with social media for this task I use a multi regex script I found some time ago on stack overflow 38133, We remove Name Cabin values 6798, Modelo 22143, YearBuilt Original construction date 30413, LSTM models 39235, BASIC PREPROCESSING 23219, assume you find some kernel with different hyperparameters then you want to check if those values are better than yours or not 16458, family size with 2 to 4 members are more likely to survive 34445, Combine the data 37192, Data pre processing 41011, And here we are 11 passengers were added to a group 18903, Sex Feature 20339, Make the train validation split 25195, we come to the most important part of our notebook 6134, Air is electrising 26874, pred gbc pd 16090, Parch vs Survival 4809, Since stacking gave us the best scores we would used that to get the predictions to be used to submit our scores 12752, Presented in the visualiztion below the survival chance of a passenger with 1 or 2 siblings spouses and 1 2 or 3 parents children is significantly higher than than for a single passenger or a passenger with a large family 4682, Since there is no NA category in the Utilities feature i prefered to fill the missing 2 values by the most common values rather than deleting the two rows Information is precious Think we have only 1459 train rows for 1460 predictions to do 4979, I limit my analysis to the letter of the Cabin 42132, Model Evaluation 27514, Reshape data 41835, Improve the performance 42525, Classifcation Report 12140, Training the model 2 22064, In the next step we calculate the fraction of words which are available in both our data and the spaCy model aswell as the fraction which is available in train and test text 12607, Ratio of Survived and Not Survived passangers for S and Q Embarked are similar but Passengers from C embarked have higer chances of survival 29153, GarageQual GarageType GarageFinish GarageCond Fill with None 10345, we add up some numeric features with each other to create new features that make sense 18385, try some more keywords and using for loop to iterate over 9663, Submission 14304, Creating the feature Age 13453, Age Missing Values 36696, Normalizing the data 9312, Again not quite what we want 12701, There s a reason I chose to look at Age after Name that s because I m going to use the new Title feature to calculate the missing the Age values a technique called imputation 11253, Submission 7480, 3f Age 42405, Baseline based on Means on the same day F1 F28 Not Active 10815, Firstly let s check what categories could have an impact on Title by building correlation matrix 22508, after training our model let s check it s accuracy 38585, Splitting data into training part and testing part 892, Random Forest Classifier 5614, On merging them I found that the merging by CHAID is wrong since they only consider SalePrice 7823, Split data for training and Testing 5063, We start by plotting all numeric features according to the current pandas dtypes 6464, Imputer for the string categorical columns 31717, Tuning LightGBM 14851, SibSp 21638, Aggregation over timeseries resample 10510, Parch 14461, back to Evaluate the Model model eval 40047, start with resnet 3006, strong DBSCAN Density Based Spatial Clustering of Applications with Noise strong font div 33802, Polynomial Features 37163, Dataset Dataset wrapping tensors Each sample be retrieved by indexing tensors along the first dimension 42223, now bring it all together 37321, Padding parameter selection 27436, Applying same Age imputation with respect to Pclass to ensure same logics in both datasets 20244, Ticket first letters and Cabin first letters are also needed 26922, First of all we need to load modules for linear algebra and data analysis as well as gensim 11753, that the skewed variables have been corrected we are going to get dummy variables for all of the remaining categorical variables 6556, Survived People with no families with them likely survived 15174, Feature engineering 17749, 3rd class passengers skewed younger than 2nd class passengers who skewed younger than 1st class passengers 35744, Another look at the feature to output correlations 33410, Defining the architecture 31909, A prediction is an array of 10 numbers 14397, Map each Sex value to a numerical value 30772, Predictions are generated as usual 24582, Create dataloaders 2457, Removing quasi constant features 16511, KNN Classifier 28005, Hyperparatemers 31075, GarageYrBlt 3705, Regression 16926, Stacking Classifier 15186, Exporting survival predictions 3425, Excellent we ll make dummy variables for each TicketPrefix category 26653, it is safe to 42265, Target Variable 1995, Multivariable Analysis 40959, Filling in the missing values 36516, Family Size 15656, Passive Aggressive 40947, Features Checking 7654, Modelling 7206, Emsemble Average Predictions 37543, As I have already told I be using Digit Recognizer data for comparison based on CNN so first lets read the data 17011, According to the plot survival rate for people with missing Age is lower than for people that have age value 12114, Pred ML Evaluation 9664, Data exploration and visualization 11052, pipeline log implemention is given below 28334, Analysis based on INCOME TYPE 41170, look at how a normal case is different from that of a melanoma case We look at somes samples from external data of alex shonenkov 30946, Training code step 29473, Distribution of the token sort ratio 31507, We segregate the data features into X dataframe and the target variable in the y dataframe 13746, Random Forest 29874, TTA 42564, First define 5 Fold cross validation 22136, GrLivArea Above grade ground living area square feet 2688, Tree Method random forest 13958, Columns 35465, Visualiza the skin cancer nevus 8397, let s use the selector in the new features 38926, Combination Vis 194, Elastic Net Regression 3540, Missing values for Categorical features in Bar chart Representation 32707, Creating an Embedding matrix 11899, ML 25957, Combine Product Asile and Department info into single Dataframe for better understanding 28331, Checking the Imbalance of Target Variable 36276, As maximum values in train set is S let s replace it with the null values 38738, This looks better as we can now guess the missing age values more accurately than before 20970, Accuracy of Model 12418, ML import with different models 13474, Using 80 20 Split for Cross Validation 5882, Deal with outliers 6194, Support Vector Machine using Linear kernel 114, fare group 29212, TotalBsmtBath Sum of 2272, Creating Categories 7504, Once weird values and outliers have been removed from the training dataset it s time to deal with missings and encoding 1916, Electrical 994, A simple method for imputing the missing values for categorical features is to use the mode this is what we ll do for Embarked 12243, NumPy basics 7291, Gradient Boosting 33585, Weighted Box Fusion 20629, Punctuations present in the whole dataset 17461, Convert categorical data to Numerical data for process 34463, Introduce Lags 34622, Time to check if our model is actually working 26632, Train the model 2763, Finding the categorical and numerical feature 15483, Choosing a Machine Learning Algorithm 41605, look at the 0th image predictions and prediction array 26575, When testing the model we also want to use a data generator to get images into the model but we do not want to transform them 31124, All data preparation process on test set 1322, Which features are categorical 32130, How to get the second largest value of an array when grouped by another array 40679, Model Reconstructions 33688, Leap year or not 19810, One Hot encoding Pros Cons 38655, Age 1986, We can check precision recall f1 score using classification report 29136, Datatype check 16088, Pclass Sex Embarked vs Survival 14252, Sibspip Feature 26971, Transfer Model 31725, Target and Genders 6192, Logistic Regression 7565, First look with Pandas 13315, We can first display the first rows of the training dataset in order to have an overview of the parameters 29946, We can also save the features to later use for plotting feature importances 10967, Dummy encoding 24988, Using LASSO for feature selection 25211, Analyze Garage Area 42221, Last up because the problem is multi class classification the network end according to the number of outcome possibilities followed by a sigmoid activation which is the most appropriate for multi class classification problems 3349, Since Deck T is negligible assigning it to frequent deck value 8165, Boosting is what the kids want these days 30921, Load data back 16445, we are done with data cleaning part 7595, other numerical features 17592, First we ll separate categorical and numerical features in our data set 14547, Gender font 37994, The frames Display Target Density and Target Probability 17606, Linear SVC 1168, There are three NaN s foor PoolQC that have a PoolArea 11860, Blending 33229, Resnets 19800, MEDIAN Suitable for continuous data with outliers 6248, Fare 32585, The best object holds only the hyperparameters that returned the lowest loss in the objective function 3496, Fit the best model and produce a confusion matrix 3514, Final Model Comparison 13452, SUBMISSION FILE choosing Gradient Boosting 18542, How about categorical variables 40192, Countplot 25999, Dropping the Middle Man 24735, K Means Clustering 17951, Random Forest Model 37114, train this model using 5 fold stratified validation 20191, Continious Featuers Data Distribution 1512, we ll try to find which features are strongly correlated with SalePrice 19014, Experiment 4 23300, Normalizing Data 15151, Embarked vs Survival 26065, t SNE 1213, Tansforming begin equation Y log 1 X end equation 953, Parameters 24967, Predict 27115, The mean and median values of MasVnrArea are significantly different 2209, Logistic Regression 21421, SalePrice 42838, Submitting the Test Predictions 31033, If you want to change match repetitive characters to n numbers chage the return line in the rep function to grp 0 n 16451, Parch 36623, Building a Sequential Model 16911, Export whole data 7897, I explore the entropy to check wheter the values can give a good learning to the algoritmh 989, let s do CatBoost 21732, I am now interest to check out how the item condition relate to the price 16273, Dtypes 18163, combining the question1 and question2 column as a single column 10992, Predict 5955, Exploratory data analysis 14643, extract information from the Cabin field by plucking the first character 30602, For each of these pairs of highly correlated variables we only want to remove one of the variables 25384, Visualize the output of convolutional and pooling layers 41201, fit this model ONLY on X train and y train as of now 27429, Imputing age by Pclass 18616, Missing Data 19958, Ticket 5596, Tuning the algorithm 28144, The list entity pairs contains all the subject object pairs from the Wikipedia sentences 33316, Submission 37668, Model compilation 39970, The younger passengers had a higher survival rate 9231, Test Model 75 15 17908, 177 missing ages in train set 28624, ExterQual 24048, Detecting missing values 17917, DATA TRAIN 32404, Convert Data to SQuAD style 22121, Took 5 mins for model to run 26549, The MNIST database is a large database of handwritten digits that is commonly used for training various image processing systems 42611, Optimisation 1 15667, Linear Regression SVC 2771, Observations 12361, MSZoning Identifies the general zoning classification of the sale 38846, Family houses are too high 856, in df test some values for Age and many values for Cabin are missing 5358, Diplay relationship between 3 variables in mesh 3D surface with solid lines 26896, Create Submission File for approach 7 22936, SibSp and Parch are two features that are closely interrelated 7240, Interpreting the Model With Shapely Values 2 2635, I couldnt find a way to hide output of the cell below 27107, Of which total numerical features are 38 17394, Embarked wise Survival probability 37370, Logistic Regression 18123, Splitting Training Dataframe prior to training ML algorithms using cross validation 30380, Add temporal features 22874, Model Evaluation 17347, Bagging Classifier 24592, Generate test predictions 8897, RIDGE 24853, Performance during training 25239, Submission 40476, Random Forest Classifier 35631, Moving in a random direction away from a digit in the 784 dimensional image space 3590, Statistical variables Ex min max mean std skewness kurtosis etc 9358, Create new feature for FamilySize which combines Parch and SibSp 23415, use the sigmoidal decay as the learning rate policy 19437, Building the Model 23952, In our dataset excluding the dependent feature we have 80 indenpendent feature If we consider all the 80 columns as our independent feature our model accuracy decrease as the number of features increases the accuracy decreases this is called as the Curse Of Dimentionality 21397, Some utility functions 30757, Learning Curve 19438, Creating Loss function optimizer and checkpoints 2740, A dendogram plot is a tree diagram of missingness that reveals trends deeper than the pairwise ones visible in the correlation heatmap 4875, Gradient Boosting Regressor 34957, Clustering 2769, Distribution of Data 1933, Garage Area 3018, One of the other methods that we tried that did not work well in selecting the feature and improving the accuracy was Backward Elimination with Adjusted R squared 22391, Predict using the model 12286, The five number summary or 5 number summary for short is a non parametric data summarization technique 11377, Decision Tree Regressor 7351, A good model is RandomForestRegressor 15701, Create categories for Age Fare 35372, Initialize loss and accuracy classes 4872, Split the data into Train and Test 10708, One hot encoding for Training data set 4735, A skewness value of 0 in the output denotes a symmetrical distribution 42424, Full Square Vs Price Doc 1707, Imputations Techniques for Time Series Problems 10434, I think we are good for now 32541, Missing Value 15930, Survived 17563, Exploratory Data Analysis 33023, Splitting 16505, step in order to increase the precision and get more accuracy I be doing more feature engineering such as trying to grab the title of the names cabin letter and ticket information 13429, Implementing Neural Network 4927, LightGBM 37902, Top 10 Feature Importance Positive and Negative Role 27134, we can analyze the rest of the continuous numerical features 9653, Since Neighbourhood and LotFrontage are highly correlated we fill up lotFrontage s NAN using it 33694, Data from M5 Forecasting Accuracy 24481, Training for 30 epochs strategy 2 improves validation score to 0 5826, Custom Implementation 17801, We also map Fare to 4 main fare segments and label them from 0 to 3 24724, Section 2 Supervised Learning Classification 10904, Check the Feature Importances returned by the Tuned Decision Tree 15696, Most passengers were traveling alone 68 43292, score 0 5561, using KNN K Nearest Neighbors with number of neighbors 5 to impute missing values 36947, One Hot Encoding the Categorical Features 21072, Testing our technique on small corpus 38948, Putting the principles in practice 22354, Extracting the test data from the test 15303, K Fold Cross Validation 42352, look like no relation of frequency hence using label encoding for it 29095, Create fake predictions 15016, Parents Children 19852, Combine discretisation with label ordering according to target 8363, There are several imputing techniques we use the random number from the range mean std 17401, We must understand the type of problem and solution requirement to narrow down to a select few models which we can evaluate 3788, GrLivArea SalePrice and TotalBsmtSF SalePrice is linear related those 2 continuous variable is both about area square feet and they are highly related to house SalePrice 3361, iLoc iloc returns a Pandas Series when one row is selected and a Pandas DataFrame when multiple rows are selected or if any column in full is selected 7510, Data preparation 590, Load input data And combine the available features of train and test data sets test of course doesn t have the column that indicates survival 21559, Age 9224, Visualize the Best Model Fit for RFC 28162, Predict and Evaluate on Holdout Set 4914, Our Feature Engineering begins with Handling Missing Data 40427, Leaderboard 30658, we have a nice and descriptive table 10990, plot co relation between labels and some features 13026, SibSP 15358, Advanced Uses of SHAP Values 24998, Adding together numeric and categorical columns 16779, Submission File Preparation 10639, How to use as feature in prediction model 24005, create a barchart to check the number of digits in each class 1982, We sum of family member 10350, Transformation and Scaling 11978, After making some plots we found that we have some colums with low variance so we decide to delete them 18657, Image Augmentation 23373, With the labels extracted from the data I now need the images loaded as numpy arrays 24968, V10 prediction 10497, So out of 891 examples only 342 38 survived and rest all died 16033, For training model we use scikit learn package 38964, Sample visualization of predictions 38702, Probability of melanoma with respect to Mean Color 32558, Sex 10120, Most people from Southamptom belong to class 3 that s why they have the lowest chances of survival 521, Seaborn Countplots 24306, start train 13287, Random Forests is one of the most popular model Random forests or random decision forests are an ensemble learning method for classification regression and other tasks that operate by constructing a multitude of decision trees n estimators 100 300 at training time and outputting the class that is the mode of the classes classification or mean prediction regression of the individual trees Reference Wikipedia 11044, Make predictions with trained models 7526, Although IQR method suggest several outliers for now I m going to focus on outliers with remotion recommended by the dataset author 1842, Identify Types of Features 13763, Tune model using feature selection 16866, Tuning model 21689, Qual a distribui o dos valores categ ricos entre as amostras 30780, Make prediction using model trained previously 11540, Random Forest 22860, Plotting Ideas Number of unique items sold each month 15641, Feature Importance for random forest 22, GridSearchCV SVR 15793, There are alot of missing values present in both the datasets which is not good for our model 27576, ps ind 15 x ps ind 06070898 30961, Some of these we do not need to tune such as silent objective random state and n jobs and we use early stopping to determine perhaps the most important hyperparameter the number of individual learners trained n estimators 8999, I definitley want to drop the Utilities column 4198, Custom numerical mapping for ordinal categorical variables 36594, Train and predict 38903, Sunburst Chart 15616, Visualize Age Data 17429, get the s 40036, L focal sum n N sum k 2 alpha k cdot t n k cdot gamma cdot log 37462, Machine learning 6431, Reading the data 13931, Some modalities can be grouped 14604, Support vector machines 41700, Some appear to have weekend like behvaiour 28365, The Dependent Variable target 5496, Random Forest Regression 36668, Very interesting Through just basic EDA we ve been able to discover a trend that spam messages tend to have more characters 37660, Save predictions 41698, Minutes looks pretty promising 29680, Data Preprocessing 12901, Modeling 12916, View statistical properties 20176, Using all data from train and test files for Submission 4696, This transformation is important Because it s the target feature 2305, Create calculated fields from other features 2 ways outlined below 11003, Parch 30350, I use a model from a marketing paper by Emmanuelle Le Nagard and Alexandre Steyer that attempts to reflect the social structure of a diffusion process 26509, To predict values from test data highest probability is picked from one hot vector indicating that chances of an image being one of the digits are highest 4982, Cleaning 30542, Baseline Model 43021, Gradient Boosted Tree Classifier 32817, Show influence of economical factors on housing prices 28081, Pipeline 30896, Plot the location where missing the regionidcity values 228, Model and Accutacy 42095, Here labels are the digits which we have to recognize 39971, More passengers with 4 family members were rescued 7494, 4f Sex Mapping 8308, Neighborhood vs mean SalePrice 24751, Transforming skewed feature variables 40844, Variable Description Identification and Correction 1774, Separating dependent and independent variables 2617, Model and Accuracy 4524, Stacking models 25961, Most Reordered Products 32121, How to filter a numpy array based on two or more conditions 3603, Distributions 13601, One hot Encoding 24300, Build Neural Network Model 32214, Add lag values for item cnt month for month city item 33515, Andorra 4225, Frequency Encoding 34653, Retrieving shop types 22651, Log Loss 21945, Python 20595, Data Interpretation and Visualization 38229, Benchmark 6551, Crating function for barchart 8030, Age 12118, We fix our skewness with the log function 2269, Ticket 9605, Family Size Details 33332, FEATURE 5 AVERAGE OF DAYS PAST DUE PER CUSTOMER 14866, Notice we only need the first letter of the deck to classify its level 13316, we can display the data type of each feature 31066, Data with new Features 42810, Prediction 15729, Random Search Training 17694, USING CHI SQUARRED METHOD 3303, correct SalePrice with a simple log transformation 38052, Effect of LandContour on SalePrice 42845, The Netherlands 2514, Logistic Regression 21618, Create one row for each item in a list explode 25762, Before submitting run a check to make sure your test preds have the right format 959, Create a dataframe from the lists containing the feature importance data for easy plotting via the Plotly package 23245, We have 3 missing features 26756, Preprocessing 25084, How about HOUSEHOLD 20739, KitchenQual column 6944, Apply visualization method TSNE for clustering our data 40612, look at the straight average 6023, Parameters 2523, Random Forests 24532, Total number of products by segmentation 28912, It s usually a good idea to back up large tables of extracted wrangled features before you join them onto another one that way you can go back to it easily if you need to make changes to it 8776, High Fare mostly have chance to survived 6308, Random Forest 2476, Training 23262, Categorical Categorical 9034, The easiest way I know to summarize the variables is by using the 21783, Below is the evaluation code for the model 20587, KNN 5473, Can we Quantify the of the decisions with in a Tree 4568, get some info about the Target Variable 27406, Prepare Submissions 2418, That should take care of the last couple of missing values 37624, Loading the data 26210, We write a function to check if the number of unique image ids match the number of unique images in the folder 9364, Save wrangled training and test data to files 26179, Rearange the prices from lowest to highest 31218, Data cleaning Preprocessing 42754, In order for keras to know which features are going to be included into the Embedding layers we need to create a list containing for each feature the corresponding numpy array 13 in total for us The last element of the list be our numerical features 173 and the categorical features that we decided not to include in the Embedding 3 for a total of 176 distinct features 42279, Show some image 25723, Hyper parameters Setting 38762, Decision Tree 28487, Reducing columns to type int8 if possible 36548, Exploring top features 15934, Embarked 11928, You can combine precision and recall into one score which is called the F score 29420, This is just a fun part I loved this thing i found in one of the notebooks so i added it in mine 6050, MSSubClass a lot of classes that I don t know the meaning of 20691, Develop Convolutional Neural Network Models 34296, This is the input image that be used for the convnet visualization 31041, Only Numbers 28935, Ops you want the hidden layer variables right 37072, Dropping Features 31224, Features with positive values and maximum value less than 10 29837, Display summary statistics for order data 41050, About Customer 40928, Sample Fold Generator 25410, This Loss function works well but we need to be careful with the log because the log of the number zeros isn t exist so we consider log 0 as the lowest value inf after use the np nan to num 21466, To create a submission file 9063, FireplaceQu 29882, Submission 37021, Prices of the first level of categories 27583, Adding basic features 35251, These plots can explain the distribution of jaccard score 1352, Convert the Fare feature to ordinal values based on the FareBand 28672, PavedDrive 40426, Run AutoML 43105, Label encoding 25827, Comparing to the Santander dataset 9286, There is a positve coorelation between Fare and Survived and a negative coorelation between Pclass and Surived 22327, Removing Punctuations 20366, Visualising the MNIST Digit set on its own 30669, Start with simple sklearn models 30574, Function for Numeric Aggregations 22705, Training and Evaluation 32722, Zeros and ones are just dominating 11640, Adaboost 29889, Finish getting the data ready to fit 29782, Encoder 8697, Price varies with neighborhood 37149, MODEL EVALUATION 42414, Visualizing Datatypes of Variable 12479, Name and Title 41473, Plot a normalized cross tab for AgeFill and Survived 29066, Numeric features basic engineering 23061, The beauty of pytorch is its simplicity in defining the model 17781, create a Decision Tree model to predict missing Fare value 10603, Survived is Output Variable 35510, First part is finding missing values for each feature 41545, How did we do 17261, Age Feature 36058, Feature Weekday 14902, Sex Pclass Fare and Survived 12412, CentralAir 43399, Prepare natural and non natural fooling targets 1728, Plot Gender against Survived 40611, Anisotropy of the space 37646, Adding interest acc to manager id to test acc to train data 18573, Gender 39217, Do the training 29937, Correlations for Random Search 39129, x frac x bar x sigma 17733, Prediction and submission 12522, For missing values 19047, Lets create a dataframe with a few features we want to explore more 38897, Predict 3363, One more thing to make note of is the higher in terms of degree you go in Polynomial the the more feature it create and you might end up breaking your code so it is better to add a break for this model 21649, Convert continuos values to categorical cut 7400, BsmtFinSF1 is highly skewed and its distribution is not normal based on the Shapiro Wilk test 6863, Feature Engineer Dealing with 0 values 32667, The charts below illustrate the dependent variable distribution before and after log transformation 41610, before we make the the days since reorder dictionary we need to address the NaN values in that column 9859, Total data table value counts 27893, Data Augmentation to prevent Overfitting 9736, Import Libraries and Settings 6467, Preprocessing pipeline that takes the raw data and outputs numerical input features that we can feed to any Machine Learning model we want 26882, Score for A2 16546 25212, Some Outliers after GarageArea of 1200 12384, Line Plots for all continuous data fields 13732, COMPARING TRAIN AND TEST DATA 9741, Pclass Ticket class 43291, Prepara arquivo para Envio submiss o 22127, Boosting in Depth 23648, Get OOF and CV score 16731, correlation 23183, Findings RF DT ETC and ABC in particular give some features no importance zero importance On the other hand GBC give all the features more or less importance but it doesn t give zero importance to any features These are the tree based models that have feature importances method by default LR KNN and SVC don t have this method In this problem SVC uses rbf kernel only possible for linear kernel to plot feature importance so its not possible to view feature importance given by SVC Though its trickier we would try to get the feature importance given by LR 35109, We can construct the following table 13986, Missing values Age 12386, Here in order to make the machine learning model I have taken the threshold to be 0 5296, there are 221 features due to a large amount of the dummy variable 25806, I think this high diversity should be accounted for when building our predictive model 20845, We ll back this up as well 33767, Setting the Random Seeds 31253, Scaling 11830, first find out the columns which have missing values 22614, All apps are installed 33481, Join data filter dates and clean missings 28016, There are also non english featuers 22836, Before we do that let s find out more about the 5100 items in the test set 2423, Defining Training Test Sets 4332, Embarked 15839, Sex 4189, Important numeric variables OverallQual and GrLivArea 30674, tokenize the tweets 23428, Common words 867, third model 14486, now appending both the test and train frames first 20967, Adding Output Layer 2643, we have removed 9 rows 5241, Converting Some Categorical Variables to Numeric Ones 27309, Here 1 is black and 0 is white 5294, For the categorical features I transform them to dummy variables but I ll drop one column from each of them to avoid dummy variable trap 20495, External Image Embeddings 2019 comp data 42225, Callbacks 15970, SibSp Feature 31104, Observing next 2 variables 22505, Checking accuracy with foloowing 21951, CNN 11105, Heat map of highly correlated Features 35379, Predicting and Submitting 38669, Random Forest 30373, Data Augmentation 10392, Random Forest 11119, Calculate Training and Validation Accuracy for different number of features 23454, Month 34696, Lagging target variable 24155, Organization informed that Not all images include wheat heads bounding boxes 10917, Ensemble 20180, Predicting Test Data 19381, Missing value 28345, Analysis Based on EMERGENCYSTATE MODE 3255, Box plots Neighborhood 15387, It is commented because I dont reccomend this 16640, Train Test data splitting 15924, Load and check data 32851, Data preprocessing 28053, Alternatively we can select a few columns and inspect within Spark 5029, Nice We ve improved our Train and Test score and reduced the difference between the two suggesting we are dealing with overfitting 5983, Bagging Classifier 11709, we are making Feachure Engineering for train and test 16275, DMatrix 22615, 0 of apps are inactive 16128, Create Dummy Values 15657, Perceptron 7371, I join the columns Hometown and Home country so that all 3 tables have the same structure and can be later concatenated 16932, And if the class distribution in the survived who survived is approximately equal most of the third class passengers died 7724, In the following features there are very less missing values so we ll impute them with the most frequent value 12844, Confusion Matrix 36207, Comparing Scores 40634, Keyword replace NaN with string 24670, Prediction on test data 28701, If you hadn t figured it out already our brick laying friend enticed us in earlier on in order to explain Stacking 18986, Doing the same things that I have done for countries 2652, People with family have 20 higher chance of survival than people travelling alone 28093, Input data are in 1 D we re shape into 3 D matrix 38706, Correlation Matrix 31269, Rolling Average Price vs Time for each store 5301, Eliminating samples or features with missing values 12942, Do the same with test set 3410, extract the letter character and make it a new variable Deck 17934, If Pclass high probability of survival is high 13754, Graph distribution of Pclass w r t other variables fare age family size 25884, Histogram Plots of number of punctuations per each class 0 or 1 23119, Findings 2235, Outliers Analysis 42948, Some Predictions 17773, Survival rate increases considerably with the fare value 2397, Using make pipeline in a ML project 21471, For swapping words we just randomly select an index of word before the last and swap it with it successor in the sentence 34739, Applying T SNE non linear method to LSA reduced space 9076, we have rows with Wd Sdng HdBoard and HdBoard Wd Sdng 16756, Feature Engineering 8222, Replacing NaN values for categorical features 12152, on to training and prediction 20729, MasVnrArea column 19016, Predict test csv and submit to Kaggle 23618, Transform category features to dummies 27856, Fill missing values in test set 17863, Build the second level ensamble model 17664, Observations 25242, Square Root Transform 37866, Data Visualization 38714, make sure tensorflow is doign all of it s operations on the GPU and not the CPU 29770, Submission 16643, SVM 35566, we add Faron s features 31752, Submission 8904, Blending the Models 18888, We are slowly getting our dataset ready 2784, Model Building 28885, In addition to the provided data we be using external datasets put together by participants in the Kaggle competition 28779, The number of words plot is really interesting the tweets having number of words greater than 25 are very less and thus the number of words distribution plot is right skewed 5847, Surprisingly only 2 features are dominant OverallQual and TotalSF instead of using all the 77 features maybe just using the top 30 features is good enough dimensionality reduction in a way 18474, Before deciding how to treat this we know there are infinite ways of filling missing values 34970, Testing Random Forest Parameters 32692, Evaluate model accuracy on the validation dataset 16585, Lets check which Fare class Survived along with their title 648, It s Mr Osen and Mr Gustafsson on Ticket 7534 40460, Average Sale Price 112, I have yet to figureout how to best manage ticket feature 34362, Temp Atemp and humidity are normally distributed 13662, Dropping 19147, Wrangling non numeric features 11862, Submission 5567, Big Congratulations now we don t have any missing values or outliers in our data 15085, SVM 3957, Creating Extra Features 39207, Submitting Predictions to Kaggle 9824, Parch Number of Parents Children Aboard 25833, Target Counts 37084, implement soft voting ensemble in mlxtend 38696, Image Size 28385, Economic Impact of COVID 19 on India 2816, proc df replace categories with their numeric codes handle missing continuous values and split the dependent variable into a separate variable for the max n cat is to create dummy variables for the categorical column with less or equal to 10 categories 17428, Invert the Survived Value 491, Age Feature 9343, Run GridSearch optionally 14765, The score for the new training sample is very close to the original performance which is good 12711, SibSp Parch 22504, Using AdamOptimizer to minimize cross entropy You can also use GradientDescentOptimizer inplace of AdamOptimizer 40794, The char 38 21033, PPS matrix 9954, Train Test Split 35571, Sales broken down by time variables 36465, Images from ARVALIS Plant Institute 1 29123, Preparing data for Pytorch 5084, compare this to the predicted values 30762, Score features 23268, Age 4322, Age 37091, Substracting normality 7342, XGBoost 19887, Here we were able to generate lag one feature for our series 6129, Square feets 29604, Interestingly the most incorrect was an example that is incorrectly labelled in the dataset itself 11990, Firstly call kfold for cross validation 15605, Make first Submission 18353, linear regression 4898, criterion A function which measures the quality of a split 25441, Evaluating the Model 4177, Top coding important 15982, Embarked Feature 1724, CODING classes gather here 10686, Regularization Models 42393, Do stores sell different kinds of items Nope All stores have the same kind of items 21355, Model Sumary 11515, Grid search for SVR 14780, Data poinst for SibSp 1 are very few based on survival ratio for SibSp possible features can be 43377, Tensorboard Visuals 13916, It looks good so let s proceed with creating target and features array 14401, Map each Embarked value to a numerical value 5103, Feature visualization with Target variable 9898, Embarked 7031, Garage condition 4652, Quick Observations 4314, We could conclude first class passengers were given preference over the other classes given the time in the history of the event 2914, I Create a copy of datasets 38425, It works 4611, One thing to note is that the tree based models took approximately 3 6 seconds to run using CPU only That is quite concerning given the fact that there are less than 1500 training samples When we perform model selection and hyperparameter optimization the running time is going to scale remarkably It s better if we can somehow reduce the complexity of the models and save that precious training time and one way is to continue cutting down on number of features 38592, Predictions on test set 18329, Fundamentally variables in the dataset can be subdivided into 14595, Fare Values 1976, Apply the Estimator which got from parameter tuning of Random Forest 39212, Since we use a neural network we need to scale the features 3897, Box and whiskers plots 11292, Turn categorical variables from integers to strings 2048, RandomForest Model 9471, Titanic Data Report 11106, Remove multi colinear features 18996, Train 7722, Imputing Missing Values 36841, The only thing I changed about the model is the size of the LSTM and GRU 22958, Submit 36767, Compiling the Keras Model 5943, fit our training data into Model 8355, Investigate who were masters 26363, Fitting the Network 8972, convert values 21789, Correlation with TARGET 32837, We have succesfully pre train buid and train our model 9052, Garage Area 34645, Creating Our Model 34632, We removed outliers data points 28171, Part Of Speech POS Tagging 15300, Decision Tree Model 33228, nbsp nbsp InceptionNets 42968, Testing Different Models 35167, Experiment Batch normalization 24758, Ridge Regression L2 Regularisation 7369, Just like with the previous DataFrame I add the column Class but assign it to 2 20470, Days employed distribution 3663, Tansforming begin equation Y log 1 X end equation 26973, Run Training and Validation Step 32055, After fitting the model plot the feature importance graph 9782, Train the Algorithms with Optimized Parameters 16725, Parch 2222, Type of Zoning 20341, Sidebar on Data Augmentation 32670, Identifying relevant outliers with regression assistance 6199, Gaussian NB 16949, its time to train We use Adam optimizer and Binary Cross entropy as the loss function 6948, Important features on cluster plot 9239, Descriptive statistics summary 38850, Generally dropping the columns is not adviceable but in our case the following columns are having too many null values 38068, Unigrams Analysis 31560, Submission 9764, Check for Correlations 41666, Feature selection and engineering 20145, Model building tymm 7497, Calculate accuracy of each model 22634, Function to store output file as CSV 19632, Age distribution lines denote target groups 18682, let s change our working directory to kaggle working and take a look at its contents 9737, Import Dataset 24324, Apply log1p to the skewed features then get dummies 40433, Submissions 25015, Resampling 7918, Basic Class DataImputer to pre clean the dataset not used here but it be interesting to compare the performances of our approach with this simpler one TODO 8501, Missing Data Assessment 23808, Separate Cat and Num 20471, Days of registration distribution 243, Model and Accuracy 26692, Train model 32886, Normalizing features 36658, Erosion is the opposite of dilation where it scans for fits among the boundaries and strips a layer from the inner and outer boundaries of the shape 36638, we try Logistic Regression another very popular classifier It s a Generalized Linear Model the target values is expected to be a linear combination of the input variables for classification where a logistic or sigmoid function is fitted on the data to describe the probability of an outcome at each trial This model requires in input set of hyper parameters that can t be learned by the model Exhaustive Grid Search comes to the rescue given a range for each parameter it explores the hyper space of the parameters within the boundaries set by these ranges and finds the values that maximise specific scoring functions 24581, Dataset class 3538, Missing values for all numeric features in Bar chart Representation 7760, Deleting more outliers 27559, Lets take a look at the dedups Don t worry about the key but just take a look at what values are in the same key 13123, Using catplots 18030, Best model parameters 11019, Replacing the age bands with ordinal numbers just like we did in Sex and Salutation 9129, Set Fireplace Quality to 0 if there is no fireplace 21599, Create a bunch of new columns using a for loop and f strings df f col new 28431, Item name correction 28363, Loading Data 30096, And here are some quick examples of the test data 7648, BsmtFinSF1 BsmtFinSF2 BsmtFullBath BsmtHalfBath BsmtUnfSF GarageCars GarageYrBlt LotFrontage MasVnrArea TotalBsmtSF are numerical 37412, We can keep only the features needed for 95 importance 20822, We turn state Holidays to booleans to make them more convenient for modeling 15819, Get Dummies to convert categorical data into Numerical data 14544, Numeric variables PassengerId Age Fare SibSp Parch font 38639, Importing Libraries 37202, LightGBM 19424, we need to add two new methods to our LightningTwitterModel class 31946, Make model XGBRegressor 34027, Sparse weather column 1160, We caF say the best working model by loking MSE rates The best working model is Support Vector Machine 40935, Hyper Param Searching RandomizedSearchCV 27829, Mean and std of the classes 36124, One hot encoding be done to encode the rest of categorical features 18161, checking the values 41397, CNT CHILDREN and NAME FAMILY STATUS 27627, Let create the word index 22483, Parallel Coordinates 31602, GENERATING THE OUTPUT FILE 490, Most used titles are Mr Miss Master Mrs 39078, Prediction Submission 33263, Callbacks 3744, One of the best way to visualize Null values is through Heatmap 231, Library and Data 7408, Take several variables for example 16274, Train Val Test Parity 30670, SGD Classifier 38457, quick tests 15378, Doctor Rev are not exactly Royalty but I tried matching age group wherever possible 4687, As i did for the Basement features with an extraordinary logical thinking p i ll set the Garage Sizes to 0 for houses without garage 2569, Confusion Matrix 8131, Correlation matrix 35806, Making the same adjustment on the test data for our submission 9661, Modeling and Predicting 16729, by age 27837, Architecture layers 26397, In the third class there are twice as much passengers than in the first and second class respectively 13034, PassengerID 28416, Variable Importances 11266, All of this means that the Age column needs to be treated slightly differently as this is a continuous numerical column One way to look at distribution of values in a continuous numerical set is to use histograms We can create two histograms to compare visually the those that survived vs those who died across different age ranges 33492, Andorra 17956, Logistic Regression 18651, Please note that there is a new value NA present in the test data set while it is not in train data 12315, It looks very similar to TotalBsmtSF 19713, Choosing a model 8134, In this part I keep the common columns between the test and the train and I delete the outliers in the train data 10539, merge the numerical categorical feature with final test c 40458, NeighborhoodOverallQualNCond 1784, Decision Tree 1417, Cabin vs Survived 22632, Here Android games are most negatively correlated with other variables of interest 39889, Ensemble 32530, Generating csv file for submission 40249, Ground Living Area 19957, Because of the low number of passenger that have a cabin survival probabilities have an important standard deviation and we can t distinguish between survival probability of passengers in the different desks 10132, Multi Layer Perceptron 27978, Check missing data for test train 18734, RandomForestRegressor bootstrap True criterion mse max depth None 35054, Complexity graph of Solution 1 3310, Delete outliers 39020, Drop the rows with a null value 42467, Creating dummy variables 35140, Data preparation 32089, How to create a 1D array 31432, incorrect prediction x start x end We can check Jaccard leq 0 19303, Data Preparation 4662, To Know The Relationship with numerical variable 36005, to create the submission 10086, Feature Engineering 38485, reflexes to have font 647, Almost 100 28775, Lets look at the distribution of tweets in the train set 32844, Evaluation Functions 25872, Number of tweets according to location top 20 15591, That took a really long time 27430, Taking log of Fare and Age 19797, Reading in the sample submission file 7287, DecisionTree Model 24460, Augmentations shonenkov using in his kernel Training CV Melanoma Starter cv melanoma starter 7224, Garage Null Values 24039, Forming splits for random forest and knn 13507, Parameter Tunning 18261, the SHAP explanation 4020, BsmtQual Imputation 30944, Correlation Between Price and Other Features 32914, The function below allow to stop the model training when there are too much epochs without improvment performance 22163, Pipeline contVars taxes FeatureUnion intro 1756, Imputation for Test set age 41603, the model is most confident that the number on this image is a 7 4481, Logistic Regression 7341, SVC 11233, More feature process work get the title from the names and map to a new feature cut the fare and age into category 34009, No outliers 28793, It s Time For WordClouds 5156, Regularised Linear Regression 41406, deliquencies 38712, Prediction 18401, Unigrams 15185, Feature importance 2217, Splitting the Variables into Different Categories 20786, Bivariate Analysis 32262, Relationship between numerical values 1244, SalePrice the variable we re trying to predict 27654, Split training and valdiation set 15923, Importing Librarires 30675, Make attention mask embbeddings and updated features 36882, KNN 8695, CATEGORICAL FEATURES 27143, MSZoning Identifies the general zoning classification of the sale 12521, we need to correct the skewness of the price 12983, Droping PassengerId and Cabin 7488, we want to group the ages 5289, Data Preprocessing 12353, BsmtHalfBath Basement half bathrooms 30283, Recovery Count 50 35417, Calculate the Mean Absolute Error in Validation Data 18660, Compile Model 21672, Data part 38644, Deleting Unwanted Columns 27270, apply Gaussian Naive Ba to our Santander data 43120, Prepare training and test dataset for ML 38059, Taking a glance into the data NaNs values basic stats distributions 18414, Test Set Labels 35327, Importing Various Modules 6704, Categorical Features 31314, Treating Missing Values 34335, Submission 18614, Model 3 XGBoost Model 22830, Extracting City 1012, Excellent now let s split our dataset back to test train 2294, Pandas whats the distribution of the data 19974, Keras models are trained on Numpy arrays of input data and labels 4659, to visulaization dependent variable which is target variable which is SalePrice 1107, K nearest neighbors 11827, Looking at the Bigger Picture 9345, Save predictions 5344, Diplay quanitive values of a categorical variable in area funnel shape 10204, Lets take significant features as X and target variable as y 9878, I am going to use factor plot to visualize SipSp and Survived variables 16009, Feature importance 792, Submission 15181, Data analysis 20210, Compare Different Tree Sizes 2422, Creating Training Evaluating Validating and Testing ML Models 24332, we do some hyperparameters tuning 33566, Fill nans 17667, Feature Engineering Title Age left right of the boat and Sex 27480, Train the model 11480, FireplaceQu 6031, we don t have any missing value 28639, GarageCond 42226, Model fit prediction 5812, Modelling Feature Importances 39410, Age 14399, Both passengers with missing embarked were of Pclass 1 and paid Fare 80 13359, View shape of test set 8328, In this way we can convert strings to categorical values 11717, Naive Bayes 9480, Confusion Matrix 41297, Make prediction 24699, let s setup logging and the best model checkpointing 31558, test data normalization 7456, Missing values in Frame column in Test Dataset 2378, Save a model of pipeline using joblib 14530, Embarked 43299, Valida o Cruzada 31629, we get a feature vector for image 20775, Takeaways from the plot 36596, First we are importing the necessary libraries 17395, SibSp Siblings Spouse wise Survival probability 14096, Random Forest 16055, SibSp Parch vs Survived 34712, Mean over fixed category id and month 5887, Imputer 25293, Submission 27102, Import Libraries 3474, get a list of the non zero coefficients 15900, Submitting Predictions 20606, Skewness is positive so we can use medium to fill the missing values 29190, Gradient Boosting 34346, Pre processing Test Data 9408, Feature Eng Extracting letters from tickets could be important font 12249, Exploratory Dats Analysis EDA 31765, To create the users x products count table loop through the prodcut ids data to as a sparse matrix column position contains the product ids with position listed in a dict 37480, There are a lot of NLP and ML libraries out there with can take over the whole process text preprocessing 39170, How to use a custom PyTorch architecture 11381, Final submission 2876, if we compare the feature importance list and drop list 15304, Feature Importance 11112, Drop features with more than 30 null values 29543, Both of them look pretty good 27748, Missing data for test 2138, Tune LightGBM 9021, Convert null variables which actually just mean that there is no basement 32997, Save Output 40392, Model CheckPoint 9639, we shall find the Top ten most correlated features to sale price 23302, drop 90 missing value ratio 13670, Decision Tree 19443, We proceed by fitting several simple neural network models using Keras and collect their accuracy 9327, Rare ordinal features 38047, Notice that the true mean is contained in our interval 38737, One thing to note here is that unlike the Master title there is no separate category for young female passengers 22617, Apps per device 10819, I would like to check what are correlated categories with Age 23126, Looks like on average if you pay more for your ticket you are more likely to survive plot histogram of survivors and victims fare together to validate our intuition 8167, Our predictions are now ready for submission 37988, okay not that exhilarating I plan to train on 80 epochs however it stopped at the 12th epoch reaching training accuracy of 0 24940, Q Q plot of the initial feature 2765, Label Encoding All the Categorical variables 18282, DRAW CONCLUSION 31357, run 35853, Alright then lets shift the colors to binary and go to the model 16695, We can convert the categorical titles to ordinal 27099, I use focal loss rather than regular Binary Cross Entropy loss as our data is Imbalanced and focal loss can automatically down weight easy samples in the training set 36823, Here we init the vectoriser with the CountVectorizer class making sure to pass our tokenizer and stemmers as parameters remove stop words and lowercase all characters 5285, In the next series of experiemnts we are going to train a number of LR models that use only top X features as ranked by RFE feature importance feature selection algorithm 39158, But we want the CNN to predict only one class 2194, Prepraring data for prediction 15848, Age 28579, This be a very important feature within my analysis due to such a high correlation with Saleprice 31815, Define loss functions for all four outputs 5593, Split the data into training and validation sets 35924, Train the model using batch size of 32 and up to 30 epochs 3910, Handling columns with missing data 20774, Visualization of target variable via Bokeh 17892, Lets plot the new variable 10163, Subplots for iris datasets 26329, Try Random Forests 22045, We could potentially add more variables like this 29012, Have a look at our target variable 27435, Decision Tree 9333, Features with a large number of categories 41776, Weights 33280, Missing Values 9113, Location Location Location 36858, Distribution of labels 24230, This is the augmentation configuration we use for training and validation 27934, With the model compiled and the data sitting ready in the pipeline it is time to train the model 6703, Train Dataset 7304, Observation 28191, Tokenizing Words Sentences 5651, Create a PClass Fare category 24857, Check the model s performance for the beginning of April 29326, look at the loss column 20730, ExterQual column 3700, Training the model 17914, Boxplot Analysis of passengers ages in each class 15903, Missing Fare 11793, Spliting the train data 16682, The survival percentage 25992, Submission 1187, delete Utilities because of how unbalanced it is 13117, Model Comparison 1601, Correlations 29189, Residual Histogram to visualize error distribution 33299, Double Check 15607, FareBand feature 12278, Set XGB model the parameters were obtained from CV based on a Bayesian Optimization Process 43121, Scaling of features 12737, Model re training 39451, checking missing data in POS CASH balance 36734, Decision Tree Model 39887, Prediction with Ridge Model 15718, passengers were male 1863, Gradient Boosting 1626, Linear Regression with Lasso regularization L1 penalty 18090, Example augmentation pipeline 43064, check now the distribution of the mean value per column in the train dataset grouped by value of target 15590, Random Forest classifier 34369, Time for some parameter tuning 16260, A quick overview of the public leaderboard to get a feel for the competition 21034, compare the PPS matrix to the basic correlation matrix 6990, Year garage was built 32894, Output dataframe 21435, Further Tuning 15022, Sex 1839, Dealing with Zeros 156, Import datasets 7473, Immediately we notice something interesting in the count row the number for Age is lower which means that for some passengers no age is present in the table 12343, GarageQual Garage quality 34330, Modeling 17374, Fill missing values for each sub dataframe then combine all the sub dataframes into one 41669, The final Random Forrest model is instantiated with the optimal hyperparameter arguments derived in section and trained using the features selected in section 4102, MinMaxScale 37306, Term Frequency Inverse Document Frequency TF IDF 8824, Feature Engineering SibSp Parch IsAlone 14982, Extract Title from name column 16435, Embarked 21191, Initialize the parameters for an LL layer neural network 18838, Visualising the MNIST Digit set on its own 15785, K fold cross validation 3450, The T Deck looks like an outlier 14054, Model evaluation 5348, Diplay distribution of numerical value in sequential way 16053, Passenger Class Vs Survived 28288, prepare field vocabs for tokens in trainset 124, After Scaling font 34464, Mean Encoding 16221, Now we identify the social status of each title 15818, Data Wrangling 27946, OOF Evaluation 249, Model and Accuracy 42472, Feature scaling 29518, delete keyword and location 7587, Indexes for the outliers are the same like for GrLivArea 20309, Here is how our clusters appear 7446, Read in the data 14600, We make the Survived columns as the features value 36269, Fare vs Embarked 1708, I won t go much into explaining the data since I have done a lot of relatedw work in my kernel titled 10750, Check the response variables 35322, Ensemble Predictions and submit 34926, Words features 23803, XGBoost SHAP Explain the Unexplainable 25914, Show orders visualization 2904, Importing Data 8320, Cross validating the model with the best hyperparameters on the train data and using it to predict the test data 6841, Models Stacking 15737, After looking plot I am thinking that Cabin feature is not important to predict Survived I want to drop it 22161, Testing 28188, that we re all set up it s time to actually build our model We ll start by importing the LogisticRegression module and creating a LogisticRegression classifier object 16832, Finding Optimal Cutoff Point 40638, keyword feature 19874, Outlier Detection Removal using Scatter Plot 33360, Visualizing nulls values 33874, Light GBM 40464, Floor Sizes and Room Conditions 37449, We load the Distilbert pretained tokenizer and save it to directory 24766, XGBoost 12067, Numerical Features 412, Bagging Classifier 36733, Building models 17474, GradientBoosting 5850, XGBOOST 6355, Last thing to do before Machine Learning is to log transform the target as well as we did with the skewed features 17657, Observations 28549, Introduction 25350, Keywords 4991, Model Selection 20752, as data is highly skewed so i decided to remover these three column 3SsnPorch ScreenPorch PoolArea 36968, ROC AUC Curve 21100, try to plot sales for every year to understand about seosaonal data 18255, Train top layers 21904, Naturally overall quality of the house is strongly correlated with the price 5113, Target variable distribution 8878, In order to handle NAs we replace the nulls in the columns of datatype object with the mode of the respective column whereas for the columns for datatypes integer or float we replace the nulls with the median of the respective column 33565, Delete outliers incredibly large homes with low prices and drop SalePrice column 27871, Ensemble 27057, Write loop to find the ideal tree size max leaf nodes 38616, Generate submission 21155, With these parameters we achieve the RMSE 26 949 24268, Observations 19275, let s make a validation set we can use for local evaluation 25459, sort predictions to have the same order as the submission 19650, Benchmark predict gender from device model 36771, plot the accuracy vs no of epcohs and loss vs no of epochs curves for a better insight 29864, To work with pixel data intelligently use pixel array tag 31936, Prepare the data for use in CNN 23825, Taking Log Transformation 24265, Dropping features 36345, Compile and Train the Model 23580, Retrain the model with the optimal hyperparameters from the search 16617, Pclass 14331, This first function creates two separate columns a numeric column indicating the length of a passenger s Name field and a categorical column that extracts the passenger s title 19699, Fit the Model 27339, Model Preparation Libraries 40432, Save Leader Model 2471, Adding Family Survival 36204, here we have the dataset with all the predictions and targets 29851, Select and train a model 42368, After a lot of experimenting I went with the random forest model as it gave the best accuracy 41367, Sale Price NA Pave Grvl 26102, Random Forest Regressor 22754, let s examine a case where R0 is reduced to 3 24909, Confirmed COVID 19 Cases per day in Germany 13066, Feature X and label y selection normalization of data to give data zero mean and unit variance X submit is the data for final submission 23886, The correlation of the target variable with the given set of variables are low overall 22014, Run the next code cell to get the MAE for this approach 24405, Permutation Importance 26844, Hook to extract activations 35446, Augmentation 7642, log transform target variable 25345, we should evaluate the model on the test set 10838, this features have good correlation with target variable 22278, It s a good thing that we filled fare with the median value as there is an outlier 10757, Another way is to do it with apply 12165, Min Sample Split 6754, Checking Skewness for feature LowQualFinSF 33649, As I am happy with the input data I won t be making any further changes to it 21898, Something to speed things up a little 31843, Category dataset preprocessing 13088, Missing values 6936, Firstly drop most correlated features 19039, Specify the folder that contains the training images train and use fastai2 s method of accessing the DICOM files by using get dicom files 14641, There is a null value and it makes sense why There was no one in the dataset that was a female with a title as Ms 8718, Deterministic and Random Regression 29018, Fare 14312, Or You can also use the below code for writing the output 38709, Training Xgboost 31697, Categorical Data 1971, AdaBoost 27587, Trick we can even spell check words although this is very computationally expensive 16972, Apparently the data of passengers having more than 5 siblings are outliers we filter our rows and keep just the SibSp less or equal to 5 18588, The learning rate determines how quickly or how slowly you want to update the weights 12632, Feature Correlations 4694, Most of them have a positive skewness 13159, Scaling down features 21758, Looks like these are all new customers so replace accordingly 23233, You have also learned how to use pipelines in cross validation 11520, Kernel Ridge Regression scores 31552, Featues Engineering 40738, One Hot Encode 23900, Convert Text to Tokens 15046, Passenger with Title Mr have a very small survival rate Rule of Women and Children first is obeyed 4261, Fillna for categorical variables 37372, Gaussian Naive Bayes 10633, Lets try to find out best parameters for SVC using GridSearch 33331, FEATURE 4 AVERAGE NUMBER OF TIMES DAYS PAST DUE HAS OCCURRED PER CUSTOMER 15634, Survival by Family Size and Gender 8412, Bathrooms Features 36385, For the comparison I m using 2 models whose hyper parameters have already been optimized 30118, create a new data object that includes this augmentation in the transforms 17770, Majority of the passengers were between 20 and 35 years old 28462, Columns regionidcity regionidneighborhood and regionidzip 36421, Features with mostly single value 16120, Random Forest 4631, Filtering null and not null values 15691, Passenger class vs Survived 14980, Creating new variable Family Size Alone 8238, Fixing missing values 15246, Logistic regression 32201, Clean item type 4817, Standard step Remove id columns from both data sets since we are not going to need when modeling 7667, what do we do in combine data that contains less than 80 missing values 12200, However in the test set we don t have all those values 30766, they all score relatively close 7742, predict 36129, Visualization of data is an imperative aspect of data science 3563, Long tail formation to the right 36812, we take this POS tagged sentence and feed it to the nltk ne chunk method This method returns a nested Tree object so we display the content with namedEnt draw 706, fill the NAs with means 33579, Model Instantiation 13096, Box plots Outlier detection 10073, Extracting first n e g 6 models 34862, 177 entries in the set are null for the Age column to save deleting imputing so many rows of data I exclude this from the features for now and include it later on 22766, Split the data into training and validation sets 11072, Feature Selection 10110, Fixing Missing Values 9274, Light GB model 40994, Filtering does not change the data but only selects a subset 9388, check outlier 11972, Dealing with categorical features 25021, That s better 23530, Accuracy and confusion matrix 17476, SVM 42624, Creating other useful columns 21891, Calculating the Attention Context Vector 7390, Below are the final lists of unmatched passengers from the Kaggle dataset kagg rest6 and from the Wikipedia datasets wiki rest6 25760, Apply 6452, we take an average of predictions from all the models and use it to make the final prediction 18374, For making the dummies command to work we need the categorical data in object or category type 9682, Make Predictions 3492, Random forest models have the added benefit of providing variable importance information 11028, Logistic Regression 10343, Correcting Features 3860, Filter out the outliers 37923, XG Boost 32878, XGBoost feature importance 11376, XGBRegressor 9893, We don t need Name feature any more 1957, Identifying Missing Value 13462, Now apply the same changes to the test data 18020, New dataframe Woman Child Group by Name features 23744, Decision Tree 28076, Ensemble Tree 7486, Group fares into 3 categories weighed according to the survival rate 8482, ElasticNet 19749, Data preproccesing 27034, Total Number of images 42088, Lets quickly look at the predictions before delving deeper into the details 40760, Use the next code cell to label encode the data in X train and X valid 36653, Averaging is done by convoling an image with a normalized box filter by taking the mean of the pixels in the kernel area and replacing the middle central element 33769, Reshape the Images 20320, The struggle is real 6916, MODELS 25319, Improve the performance 9931, I compared Linear Regression and XGBRegressor because are the only two models I worked with haha 23890, Bathroom Count 38559, The plots reveal that values among X0 X1 X2 X5 X6 and X8 are fairly distributed where as values in X3 is moderately distributed 32282, Display distribution of a continous variable 23577, The Hyperband tuning algorithm uses adaptive resource allocation and early stopping to quickly converge on a high performing model 31215, Bivariate analysis scatter plots for target versus numerical attributes 3526, Visualisation of OverallQual TotalBsmtSF GrLivArea GarageArea FullBath YearBuilt YearRemodAdd features 13352, View shape of training set 3943, Missing values Imputation 21756, Empty columns for some ages REVIEW 7892, For Is alone and AgexClass the effect in the survival rate is not that clear 13530, Automatic FE 13372, Sex 24361, This is giving us an accuracy of 92 10196, predict for test data and generate submission file 42775, OneHot 32392, Simplified Meta Predictions 29400, BUILDYEAR CAN BE IN FUTURE TYPE OF PRODUCTS 18079, Similarly let s look at very small bounding boxes 21444, Data Summary 4263, Differences 13517, After creating new features we can drop useless columns that we won t use in the training process 32916, it s time to predict test dataset 9843, Hyperparameter Tuning 18362, Vizualise the Continous features Vs Demand Count 5261, we are ready to train one more set of RF models that use top 50 features selected by permutation method 8036, Checking the Correlation Matrix 8402, Exploratory Data Analysis EDA 37012, Best Selling Aisles over all Departments 4112, Fill the features more than 10 percent of the missing data with None Values 18262, try on a few more images 13416, AdaBoost Classifier Parameters tuning 23046, so now lets normalization our data 2997, Feature Engineering Creating New Features 28195, tokenize the sample text and filter the sentence by removing the stopwords from it 20412, There are two rows with null values in question2 42362, stacking base models 10829, We have 713 single tickets and 216 that belong to family members 43340, import train test split module from sklearn 2770, Finding the outliers 38632, let our trained CNN recognize the image and get the outputs from each layer 30147, correlation 15180, also quickly check Age s relation to Survival and investigate our Embarked Pclass theory 31732, Preprocess csv files 3784, Model Comparison 11922, K FOLD Validation 30850, Location for Occurence of Larceny Theft 26769, Fit models 31427, Generate some correct true labels and arbitrary prediction lables 5652, Create a Family Size category 15989, K Nearest Neighbor 19575, shops analysis 21803, Feature importance 31133, Embarked feature 29747, The dimmension of the original train test set are as following 903, Correlation Matrix 14696, Numeric Features 13164, Scaled features 11059, Age versus Fare 3267, Data Cleaning 18970, Display the contour and histogram of two continuous values 6918, Final model fit evaluation prediction 30773, Data cleansing to drop NA row 2634, SO let s repeat it all 32836, That s how i got to from 27839, Set other parameters 7769, Non linear SVM Regression 3024, Since area related features are very important to determine house prices we add a few more features which is the total area of floors bathrooms and porch area of each house before we continue droping these numeric colum 30835, Month Feature 39118, K Nearest Neighbor 37229, We can replace Quorans with Quora contributors 5693, Prepare submission file 12672, Dropping null values from train 28175, Lemmatization 33878, Stacking Regressor 40158, In this section we closely look at different levels of StoreType and how the main metric Sales is distributed among them 40455, HouseStyle 14299, little more about the missing Values 36346, Predictions 1880, Age 14386, Most of the passngers were of AgeGroup Young Adults and Adults 22367, Cleaning Data on Column Age and Fare 622, As suspected it is more likely to know the cabin of a passenger who survived 37403, Drop Correlated Variables 12429, with regex 7114, Dealing with outliers 14868, Where did the passengers come from 36987, Using xgboost the important variables are structured tax value dollar count followed by latitude and calculated finished square feet 31637, Reload Dataset 27636, area total calc is the most cross correlated item The larger the overall property the more room there is for everything else including tax 29072, Categorical features label encoding 19871, finally we were able to remove two outliers using this Z score approach 1755, One wonders what is the point of comparing distributions pre imputation and after imputation Do they need to follow the same distribution Well to me this is not an obvious answer but one intuitive explanation is as follows 33844, Number of distinct questions 41116, Year Build Vs Mean Error in each County 14423, Fill missing Cabin data with N 3564, Skewness The longer the right tail the more positive the tail 7909, Correlation matrix 37747, There we go finally by optimizing the columns we ve managed to reduce the memory usage in pandas from MB to MB an impressive reduction 19971, VGG 16 27902, Read the Data 32769, Build the Network 30264, score favors classifier that have similar precission and recall This may be not always what we want Sometimes we want to have a model with greater precision but this as a consequence lower the recall rate We can visualize the precision recall ratio in a line chart using precision recall curve function from sklearn metrics 21434, Add Boolean columns 34737, Random 3 dimensions of the Latent Semantic Space 43349, Predicitng from model 38480, Preprocessing font 28464, Column censustractandblock 32119, How to insert values at random positions in an array 7305, Observation 13389, 537 people are travelling alone and 354 people are travelling with family 41937, Discriminator Training 23743, Gradient Booster Classifier 574, scores from GridSearchCV 13130, Survival by Embarked 10899, One Hot Encoding 36235, After analysing the model we make the final predictions and create the submission file 8252, Bivariate Analysis 12190, Transformers and classes 20763, so we divided train and test data 40704, CPU test 20938, Optimization 33078, Feature Engineering 11462, Feature Selection 13657, Binning Converting Numerical Age to Categorical Variable 40999, After maxpooling operation with stride of 2 again output from conv2d batchNorm Relu gets downsampled to half 34171, First trials 38471, Training dataset visualization 265, Preprocessing 43055, The first 100 values are displayed in the following cell Press Output font to display the plots 13508, Model Assembly 27465, Data Visualisation 1222, Box cox transform for skewd numerical data 28181, The words dog cat and banana are all pretty common in English so they re part of the model s vocabulary and come with a vector 42465, ps car 13 and ps car 15 20559, Confusion matrix evaluation of the model on the validation set 32666, The dependent variable is right skewed 40199, Model Evaluation 15199, Embarked 4860, Getting Correlation between variables 2429, Random Forest Regressor 7509, Some categorical columns refer to quality or type and can be encoded into numeric values 24185, FastText does not understand contractions 2162, Learning curves allow us to diagnose if the is overfitting or underfitting 16364, the Countess Sir Major Johnkheer Don Col Capt Rare Titles 5143, Rare labels 30182, The species are in string format as such to convert it to one hot encoding format first I have to labelling it in assistance with labelencoder 67, Train Set 30836, Hour Feature 12498, we can put the training data through our transformer We split the data into a train and test set 12164, When the tree is deep we get nodes and leaves with a very small number of samples which are therefore not very informative 12417, use correlation matrix to examin the top 10 features to eliminate similar ones 4884, annot argument is mandatory as you also need data value in each cell 20271, Unique categorical values per each category 7940, To improve further our prediction we can stack the different top regressors 1087, Load data 11915, I want to create different categories for family members 20791, DROP GarageArea 14138, K Nearest Neighbor 15956, Train set 29929, The only clear distinction is that the score decreases as the learning rate increases 42846, Russia 3417, We begin by chopping up the Name variable to give us Title LastName MaidenName Nickname and FirstName 14367, In case of female passengers Parch 0 passengers had highest survival rate but in case of male Parch 1 had highest survival rate 18442, Interpretation font div 35448, Evaluating 3780, Model 1 20462, Organization type 27335, Segregating Data 29948, Bayesian Optimization 16283, Unused columns 41735, This is where the magic happens 6756, Checking Skewness for feature PoolArea 9232, Scorecard Model 75 15 16235, For comparsion of different models we are initializing one list which store accuracy of all the models 32229, First things first we should always create a validation data set to evaluate our model and avoid overfitting 40746, Run the model 9973, The Polinominal idea 23189, RF s specificity score indicates it correctly predicts over 92 of the victims as a victim Comparing recall score with specificity it looks like our rf model is more accurate on predicting negative class victims 0 than predicting positive class survivors 1 17720, Since there is only two missing values in the train data Embarked feature they be filled with the most frequent value 16784, Naive Bayes Classifier 13603, Similarly one the encoder is fitted we can transform test set as well 33474, As a fraction of the total population of each country 25165, Basic Feature Extraction 31613, Random Forest 24507, Current distribution 32574, The number of leaves on the other hand is a discrete uniform distribution 27190, TF IDF 8369, Create dummy variables for categorical data 1776, Feature Scaling 39278, RAW FEATURES 27441, Evaluating 7467, Submission 34708, Other mean values 7420, Make sure that variables in test data have the same order as in training data 41595, Training the LGB model 10582, Evaluating ROC metrics 27387, i use a single validation set to keep it simple and fast 20405, Exploratory Data Analysis 30904, let s plot it on the map 14566, Lets convert our categorical data to numeric font 4279, Remove Outliers 24588, Check how well the prediction went 22348, Bernoulli Classifier 20216, Encode the y train labels 11801, Observation All of them have positive skew 43273, Avaliando o desempenho do nosso modelo nos dados de valida o 2249, Import Libraries 5804, Data is ready for training 14601, Logistic Regression 35429, use all our models to make predictions 4485, Support vector classifier using Linear kernel 17978, that we have titles 14485, data cleanning jobs are pending 1 filling of null values imputaion 3 oulier cleaning 14320, SEX 36303, Magic Weapon 4 All model Accuracy Score 7261, SibSp Feature 14588, We fixed all the missing information available in the dataset 16091, SibSp vs Survival 1037, Categorical features 40961, Importing ML Libraries 7936, LightGBM Regressor 2725, it s time to make predictions and store them in a csv file with corresponding Ids 3572, It is strange that FullBath is zero higher 41325, As a reference we train a single decision tree on all the pixel features and check what score we get 16999, Importing Libraries 21791, Comparison of classification models 14794, Logistic Regression Model 42100, Splitting Train dataset into training and validation dataset 4362, BsmtFinSF2 feature is not looking useful 3661, Quiring the data 7256, Make predictions 3824, We define our success in the binomial distribution as surviving so the population proportion value is in practice when performing these tests we do not have access to this value as we are attempting to estimate it but we use it as reference to get an idea of the accuracy of our intervals 42017, Creating a new Dataframe with certain columns 21273, Test Data 31808, Vgg 16 model 33782, Examine the Distribution of the Target Column 3711, XGBOOST 19780, Gradient tuning 2519, Random Forests 26092, Loading the best model 13292, Bootstrap aggregating also called bagging is a machine learning ensemble meta algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression It also reduces variance and helps to avoid overfitting Although it is usually applied to decision tree methods it can be used with any type of method Bagging is a special case of the model averaging approach Bagging leads to improvements for unstable procedures which include for example artificial neural networks classification and regression trees and subset selection in linear regression On the other hand it can mildly degrade the performance of stable methods such as K nearest neighbors Reference Wikipedia 10625, One Hot encoding dataOne Hot encoding 7343, Ensemble 1055, Linear regression 32928, Plotting model s accuracy 2383, Display the intercept coefficients for a liner model 22686, Remove unnecessary double quotes in names 18004, Machine learning algorithms need not be the blackboxes especially with the availability of various tools today 9606, Embarkment Details 40965, Titanic Deep Learning Model 28547, Train 24383, Target Value SalePrice 35533, Based on the distribution of data let us remove some of the outliers 22198, Define RNN model 31668, Display Data 36392, Alternative to the median value use the correlation between categories 15938, Fill Embarked nan values of dataset set with S most frequent value 6476, Lets check corelation of target varible with other variables 3497, Classification report 10197, Anathor such important features are kitchen quality garage quality and condition etc which include object data type 27088, LDA 7968, Correcting by dropping features 15772, ticket 4244, The overall accuracy scored using our Artificial Neural Network can be viewed below 30619, The Survived variable is the label of the dataset we want to predict 11966, Calculating how much percent of null values in each columns 8589, Here we have imported SimpleImputer created an imputer with strategy median created a dataframe of just numerical features applied fit on the numerical dataset and transformed the dataset 1537, we ll fill in the missing values in the Age feature 19670, write a short function to visualize the 15 most important features 36600, Building the model 12947, Correlation of features with target 38627, test data is loaded and converted with pre learned DictVectorizer 7922, Check the skewed features other than SalePrice which was already corrected 2555, Well the person who paid 500 pound did survive while the relationship is not linear but we do have slightly high chance of survival if we paid more than 100 pounds 42304, Training Validation Split 308, blend 50 50 29109, So the train and test directories have been created Now here are a few things to consider 21952, img 26391, back to Table of Contents TOC 29943, Meta Machine Learning 5474, Below i m using the predict function from the tree interpreter package 9963, Sales by month s 25585, Null values research 17817, Precision 39462, Rovolving loans Arrangement which allows for the loan amount to be withdrawn repaid and redrawn again in any manner and any number of times until the arrangement expires Credit card loans and overdrafts are revolving loans called evergreen loan 36809, now we can turn this into a DataFrame for better visualization 38492, we remarque that there is missing values in the trainset and testset 20558, Training progress plot 27100, Visualizing Attention 31278, Modeling 34914, Count punctuation by 38572, Variance Threshold 12714, Machine Learning 19425, Alright we are almost there 34354, let s do some fitting 28067, Creating a new column and dropping a column 21775, TRAINING MODEL 34677, Taking a look at what s happening inside the top category 42169, MNIST dataset 10646, Those are some crazy numbers just people paid almost times total Fare compared to other people 17689, FARE AGE w r t SURVIVED 6474, Analysis Sales Price Target or Dependent Variable 5308, Assessing feature importance with random forests 39063, Wiki News FastText Embeddings 15198, Sex 20275, center center 21661, Select columns by dtype 739, Unskewing 11988, explore a bit more about the variance each feature add to data 30920, Save for later analysis 23615, Treat missing values by mode of the column 17724, This explains why a high number of passengers who embarked from port S paid the low fares as this port is mostly used by Pclass 3 13576, it look s better and clearly 24282, Observations 14739, Our KNN model is moderately sensitive and highly specific 74, Embarked feature 18662, Predict 10978, Comparison 14712, KERNEL SUPPORT VECTOR CLASSIFIER 2160, Extra 42453, Binary variables 9386, check outlier 10034, Missing value in test train data set are in same propotion and same column 29094, Load wieghts for WRMSSE calculations 20039, Where X is our input to hidden layer matrix computed using the function from the previous step and y is our training labels 17798, we replaced the missing Age values with the one obtained from the approximations 6617, Creating Train Test Set 31789, Blending predictions 37373, Linear Support Vector Machine 36680, min df float in range or int default 38048, Are house prices in OldTown really different from the House Prices of Other Neighborhoods 8449, Altogether there are 3 variables that are relevant with regards to the Age of a house YearBlt YearRemodAdd and YearSold 40283, Set up network architecture 42460, Interval variables 27144, Category 3 Overall Quality and Condition 3829, selection indexing and filtering 1961, Feature engineering 284, Name 14338, go to encoding some categorical features 22427, I recommed using the OOP aproach of plotting in matplotlib and I be covering this way of doing things in the tutorial 8830, Divide df to train dataset and holdout for final testing purpose 38845, Gable supports for cold or temperate climates 1227, Splitting the data back to train and test 2057, create a dataframe to submit to the competition with our predictions of our model 7924, Apply dummy categories to the rest 19867, In this case mean is 29 32824, Test data preparation 38127, Max boarders were from South Hampton 34230, To convert it we need to add our width and height to the respective x and y 4444, drop columns that missing percent is too high or unnecessary 40108, Sequence of states 42808, Because our regression model doesn t predict briefly mentioned peaks let s create classification model for them 16516, XGBoost Classifier 3301, Bsmt According to the file fill with None or There are also some special examples below that should not be filled like this I have tried other filling methods but in the end I chose to fill with None or 34038, Describe Dataset 21277, Kaggle Submission 4312, Gender 23659, Rolling Average Price vs Time CA 30847, Visualize the Criminal Activity on the Map 41912, Exploring our dataset images size 38681, Categorize Mean Color 7477, 3c SibSp 24257, There are 4 types of titles 1869, Predict SalePrice for the Test Data 26713, first look at the distribution of prices across Categories 13383, Since Age and Fare do not have values less than 0 214, Libraries and Data 41015, We can understand the power of this approach now if we look at all the values that WCSurvived assumes 14533, Dropping the Name column which doesn t add up to our predictions 5186, The core principle of AdaBoost Adaptive Boosting is to fit a sequence of weak learners e models that are only slightly better than random guessing such as small decision trees on repeatedly modified versions of the data The predictions from all of them are then combined through a weighted majority vote or sum to produce the final prediction The data modifications at each so called boosting iteration consist of applying N weights to each of the training samples Initially those weights are all set to 1 N so that the first step simply trains a weak learner on the original data For each successive iteration the sample weights are individually modified and the learning algorithm is reapplied to the reweighted data At a given step those training examples that were incorrectly predicted by the boosted model induced at the previous step have their weights increased whereas the weights are decreased for those that were predicted correctly As iterations proceed examples that are difficult to predict receive ever increasing influence Each subsequent weak learner is thereby forced to concentrate on the examples that are missed by the previous ones in the sequence Reference sklearn documentation learn org stable modules ensemble htmladaboost 40925, Augmentation 21849, Training 41440, tf idf is the acronym for Term Frequency inverse Document Frequency It quantifies the importance of a particular word in relative to the vocabulary of a collection of documents or corpus The metric depends on two factors 32172, REGULAR FEATURES EXAMPLE 2179, No significant differences can be found 34624, k Fold Cross Validation 38099, Submission 18315, lots of items in each category 20377, Predict the test data 25425, SLATMD Almost completely repeated Removed 19634, Class probabilities benchmark 37429, Number of unique words in tweets font 1301, Observation 483, Reconcile feature types 27373, I impute the missing prices with the mean price of the category of that item 31298, Prepare Keras Data Generators 18443, Ensembling font div 5052, checking 4 more features of size 12244, Indexing 12009, before moving to next kernel let s get R squared value for it 41054, Initial test using Gradient Boosting Regressor 41247, DEALING WITH THE NULL VALUES 137, Grid search on KNN classifier 6204, K Nearest Neigbhbors 20844, The authors also removed all instances where the store had zero sale was closed 22711, Plotting the loss in variance as we reduce number of components 29933, The next plot is learning rate and number of estimators versus the score 11636, Decision Tree 37885, Prediction from Linear Model 18923, Cross Validation 25221, Group the similar featurtes related to a House Feature and analyze 15024, Age 1526, Sex Feature 12928, Age 18672, Make prediction for test data 41092, Certain Recovery South Korea Germany Iran Flattened the Curve 28785, Cleaning the Corpus 12688, Age 17933, Pclass 11218, Show adjustments 29030, Missing values imputation 4241, Genetic Algorithms using TPOT 21642, Fill missing values in time series data interpolate 29913, Score versus Iteration 10384, Imputing Categorical variables 31863, Building the new top 37177, Output the data to CSV for submission 14257, Family Size And Is Alone 18190, let s have a look what is the product with the biggest demand of all times 29586, we ll actually plot the images 30174, we ll display these heatmaps a bunch of different ways overlaid on a map 15557, Filling out the missing fare 4761, Outliers is one of the most important task in EDA 27255, Train our First Level Models 38016, The part before the double underscore is the vectorizer name and the feature name goes after that 34788, During working days there is a high demand around the 7th hour and 17th hour 37026, Which brands are most expensive 4035, The four data points on the far right side of the graph are outliers in the data set based on LotArea SalePrice distribution and we remove these four data points from the dataset 10048, Predict for unsen data set 38001, Well 8015, Few Columns datatype are defaulted as int64 but they are catergorical in nature 25284, The cnn learner factory method helps you to automatically get a pretrained model from a given architecture with a custom head that is suitable for your data 8029, There are outliers for this variable hence Median is prefered over mean 13479, Final RF Submission 32220, Modelling 31600, EXTRACTING THE FEATURES FROM THE TWEET TEXT 842, Ridge 25655, The mean degree is about 2 7263, Embarked Feature 20287, Oldest person to survive is of age 85 31671, Determine Optimal Learning Rate 33498, Germany 2482, Exploratory Data Analysis EDA 11890, Naive Ba 12965, Pclass Age and Survived 19628, THE TEST DATASET 30378, How to Tell if the Model is Good 14839, At a first look the relationship between Age and Survived appears not to be very clear we notice for sure that there is a peak corresponding to young passengers for those who survived but apart from that the rest is not very informative 1399, Age vs Survived 11012, We got the Salutations 15758, Let s take a more detailed look at what data is actually missing 37917, Model Comparision 14686, lets create some creative new features using the existing ones 26701, Plotting Sales Ratio across the 3 states 33868, Defining Cross Validation and Error Function 24423, Infrastructure Features 8269, Create TotalBath Feature 19335, Transforming testing data 2157, Preparing the data 37661, Conclusion 31367, submission 20161, Using from sklearn library 2786, Load Dataset 39041, complete this first part by looking at the averaged interest levels for group 0 7822, Ensemble by Voting 843, Lasso 12119, Dealing with missing data 24992, LASSO for numerical variables 24394, Model building 27660, Plot CNN model 30648, Voting Classifier 23852, training the whole dataset on selected parameters so as to avoid any data loss 14831, Train Test Split 33301, Modelling 41791, Confusion matrix 31506, Feature Selection using K Best 37656, Extract test data from zip file 37299, Visualization 42112, Seems farily straightforward just ID text and target firlds In addition the train set is very decently sized million records is probably enough for a decent text classifier 3558, Select a model 17535, Extract number from Cabin and create custom Room bands 13949, Numerical Variable 6310, Decision Tree 27362, Motivation the item id is not ordinal and if we one hot encoded it we fall in the curse of dimensionality hence mean encoding is needed to solve this trap 12792, Instantiating the class and Fitting the model 8885, Creating two more features of the total number of square foots in a house which be the sum of basement 1st floor and the 2nd floor 35476, Adaptive Thresholding 32733, Import libraries 3740, The correlation matrix may give us a understanding of which variables are important 23104, what does the value of skewness suggest 24944, Interactions 20076, Insight 34765, Generating predictions for Test set 24742, Yep two pretty clear outliers in the bottom right hand corner 48, Feature Scaling 25982, Bayesian optimization using Gaussian Processes optimize github io stable modules generated skopt gp minimize html 2896, The reason why we are not doing applying Dummy function here because when we apply this function individually on train and test data this may lead to different feature vector dimension becuase of different number of unqiue varaible in features Confusing let s narrow it down one simple example 23097, Findings Looks like Cabin is an alphanumeric type variable with 1014 missing obsevations There are 187 kinds of categories in variable Cabin Since there are too many categories in Cabin we must process e reduce the number of categories Cabin to check if there is any association between Survived and Cabin 42364, Visualizing given dataset 12268, Preparing to modeling 21610, Filtering a df with multiple criteria using reduce 42904, Individuals 25261, Sample images of dataset 7270, Age Feature 39135, Hidden layer 36405, model prediction again 7429, I start from the original y train to train the random forest and then use the log transformed y train to train 3650, Combining SibSp and Parch to create FamilySize feature 25070, Time series data plot FOODS 4633, Dropping columns 21633, Filter a df with query and avoid intermediate variables 15783, Decision trees 28131, Building a Text Classification model 8537, Statistical Signifiance 12019, WOW XGBoost performs very well let s make predictions on test data 10695, Cabin processing 29811, PreTrained Fasttext 14435, go to top of section eda 10231, Before predicting from test set we need to clean test data set to make it equivalent with training data set e 27842, Fit the model 28136, Fitting our Model 4483, Quadratic Discriminant Analysis 1303, Zoomed Heat Map 23951, Filling the missing values 20749, OpenPorchSF column 23051, once our layers are added to the model we need to set up 20915, Feature creation 8970, A B C T are all in first Passenger class 28294, Setup torch nn Embeddings layer 36019, Aggregator 43398, Attacking the model 23384, The final part of the data generator class re shapes the bounding box labels 28012, TARGET DISTRIBUTION 43209, test evaluation and visualization 12477, Embarked 22247, Modeling 23460, Month 2298, Pandas Filtering and slicing your dataframe 10942, Structure of train data 36463, Plotting with and without bounding boxes for same images 1378, To finish the analysis I let s look the Sibsp and Parch variables 37101, Feature Extraction 17937, Age Fare 13833, check shape of training and test set 15604, Create initial predictions 18716, let s run fastai s learning rate finder 26458, GB Modelling 32823, Set Model for prediction 17900, Lets try SVC 20129, Concatenate all features 2706, For this section we use Pipelines which are a way to streamline a lot of the routine processes 24535, Number of products by customer type at the beginning of the month 38259, Target Distribution in Keywords 29888, The first time around I weight by time 42146, Sampling Layer 6266, As expected the survival rates of females were signficantly higher than males 22009, Investigating cardinality 17639, Basic Modelling 33321, Create the neural net model 34299, The 5th channel looks like it is detecting hair or the darker color with the image of the mole basically removed 7276, Embarked Feature 10273, That looks halfway decent 4686, And 209, Gaussian Process Classifier 27840, Data augmentation 31940, Error Analysis 42572, Lets have a look at the edge cases in which we are extremely certain about our predictions or and either wrong or right 27242, Lets display the confirmed cases and pandamic spread on a world map 6138, we just fill the last missing value with TA 42532, Converting the date column to datetime datatype so that we can extract day and month from it 9734, Final Imputation 31054, Mac Address 7310, Multivariate Analysis 20515, Another quick way to get a feel of the type of data is to plot a histogram for each numerical attribute 16072, Further Evaluation 23339, Ekush Numerals 17780, Imputation of missing data 36780, Building a Classifier 4156, One Hot Encoding OHE 5164, XGBoost 685, For the purpose of this demonstration we only use the file train 10679, Data Preprocessing and Data Cleaning 9867, SibSp Survived 33227, Find final Thresshold 1685, Relationship of a numerical feature with a categorical feature Relationshipofanumericalfeaturewithcategoricalfeature 13376, Age 1268, PART 1 Exploratory Data Analysis EDA 3 14179, As noted in the data analysis part 0 refers to male 8108, Age 28444, COLUMNS WITH NON VARIANCE 32207, Add item cnt month lag features 14559, Oldest passenger Survived was 80 year old boarded from Southampton who was in class 1 font 21567, Convert year and day of year into a single datetime column 12760, I count the number of data examples in each class in the target to determine which metric to use while evaluationg performance 12141, Basic evaluation 2 42324, Transforming Train data and Test data into images labels 8014, Sumbit Test Set 32859, Feature item cnt distribution 8900, AdaBoost 29751, now plot the images 17257, Have a look at data shape 10164, Pair Plots 26753, size of image be height width 3 33663, Converting the string field to datetime is mandatory before processing them 20588, Decision Tree Classifier 38680, Mean Color Used previously saved data 34514, Plot for a sanity check 38565, Linear Model Lasso 3468, we set up some cross validation schemes using the KFold function from sklearn 18349, and a similar intrepretation can be drawn for NA in Fence FireplaceQu BsmtCond BsmtCond BsmtQual BsmtExposure BsmtFinType BsmtFinType To deal with all these NA values a simple imputation is enough 6277, We now have 5 meaningful categories 8022, Inspecting the Dataframe 41910, Balance the distribution based on the smallest set 29919, Distributions of Search Values 15731, Grid Search with Cross Validation 40079, We try to fit our data with KNN and check for the best number of neighbors 40828, perfrom some feature engineering 27484, We use a very small validation set during training to save time in the kernel 26512, On the local environment we recommend saving training progress so it can be recovered for further training debugging or evaluation 19298, Data Visualization 29144, Furthermore we could also display a sorted list of all the features ranked by order of their importance from highest to lowest via the same plotly barplots as follows 20799, FEATURE ENGINEERING 22770, We first create a list called field where the elements be a tuple of string and Field object 21246, Compile our DC GAN Model 21734, Make Predictions 7813, RMSLE on hold out is 0 923, Being root mean squared error smaller is better Looks like RIDGE is the best regression model followed by SVR GB and XGB Unfortunately LR can t find any linear pattern hence it performs worst and hence discarded 29071, Text features 24478, Strategy 1 Simple CNN Architecture 21196, Linear backward 11551, Correlation of SalePrice with Numerical Features 29537, we re gonna scale them between 0 and 1 22281, Check Test DataFrame For Any Missing Values Too 17665, Correleation 2166, Bar plot gives us an estimate of central tendency for a numeric variable and an indication of the uncertainty around that estimate 21797, K Nearest Neighbors 14126, Embarked 21743, item cnt is correlated with transactions and year is highly correlated with date block num 41467, Plot a normalized cross tab for Embarked Val and Survived 29920, Even though the search domain extended from 0 922, Model Evaluation 15351, we can finally take some insights from our model 6049, Similar distributions for OverallCond in train and test 33881, XGBoost 1786, Submission for Random Forest Model 21531, look at the connections for the first 100 rows of negative responses 5323, Display boundaries and dense by lines using latitude and longitude 3183, Fast and Butchery 19332, Training for 50 epochs 27150, RoofStyle Type of roof 41070, li style float left margin px font px Georgia Times New Roman serif padding px overflow auto 21917, For selecting most important features lasso regression is performed on various alpha values and optimum features are chosen according to RMSE score on Ridge model 36545, Random Forest Top Features 2710, We use lasso regression 24675, EVALUATING THE MODEL ON TEST SET 3906, Skewness 32057, Backward Feature Elimination Recursive Feature Elimination RFE 1712, Imputation using Linear Interpolation method 14326, Embarked 32534, Loading the weights 26286, Forward propagation with dropout 6543, categorical columns 38975, embedding dictionary starts with 1 so at 0 index nothing be there 19199, To make the visualization easy I separate the rest of the products in 3 groups with comparable number of customers 40054, take a look at the different target distributions 15440, produce the output file 22298, Not sure if information loss is worth it but experiment 21833, Split home data and test data into quantitative and qualitative be imputed differently 26903, Score for A9 16011 14832, Simple Logistic Regression 21168, Defining cnn model 26296, Defining CNN Model 9169, This looks rough 6080, so we have titles 33712, FEATURE SEX 8119, Linear SVC 28791, Look at Unique Words in each Segment 28278, Fitting a new model with the tuned hyperparameters to the combined dataset 1353, And the test dataset 42036, Groupby count cmap 32756, Installment Payments 321, Submission 2210, Random Forest 26700, Total sales from each of the state 29111, that the data is in the correct file and folder structure we feed it it to DataBunch object which is used inside the FastAI library to train the CNN Leaner class 7502, Remove the two outliers on the bottom right corner 38191, We can clearly distinguish digits facing or shifted towards one of the edges 11874, DataFrame concatination and Y separation 39715, Word2Vec retrieve all unique words from all sub lists of documents thereby constructing the vocabulary 35400, Dropping the cabin column as 50 values in it are NaN 32800, Extra Tree 39984, SalePrice vs GrLivArea 26932, A little bit modified preprocess 1344, Create new feature combining existing features 28634, GarageCars 18380, RMSLE 30336, The first function prepares a random batch and the second one prepares a batch given its indices 26847, Third batch 10031, Submission 20564, Specify and Fit Model 35907, Visualizing Training Set 25832, Looking for text and target data only 38772, Inception 12649, Predictions 37038, Can the length of the description give us some informations 34538, Fine Tuning the model for tweet classification 18557, The smallest group is honor passengers with royal kind titles 147, AdaBoost Classifier 4268, KitchenQual 41875, Building a voting classifier 27781, Defining the model 42522, It looks like diversity of the similar patterns present on multiple classes effect the performance of the classifier although CNN is a robust architechture 39260, ANALYSIS OF TEMPORAL TRENDS 19441, Training the Model 36021, Classifier 33097, Gradient Boosting Regressor 39751, Support Vector Machine 35050, Preprocessing 15451, Split dataset back into train and test variables 4187, Data exploration 38051, Fit If the significance value that is p value associated with chi square statistics is there is very strong evidence of rejecting the null hypothesis of no fit It means good fit 27439, PREDICTIONS ON SAMPLE DATA DT 33522, Further improve by auto cropping 11148, Look at some correlation values in a list format 3899, One Hot Encoding 21412, NOTE Even tough it is automatic we can incorporate some manual features IF we know some domain specific information 9894, Family Size 32426, Accuracy Function 35393, Generating Submission File 24174, Submission 33575, Configuration Settings 23759, Running the XGBRegressor Algorithm 16390, Cross Validation Plotting using CV set 16776, Feature Engineering 28296, plot training process 23374, With the ids split it s now time to load the images 2206, Train Test Split 2056, Great now that we have the optimal parameters for our Random Forest model we can build a new model with those parameters to fit and use on the test set 16020, Fare 6721, Selected HeatMap 5201, We fill NAN values in the rest of the columns using the mean values 42622, Clustering the data with those 5 columns Population Size Tourism Date FirstFatality Date FirstConfirmedCase Latitude Longtitude Mean Age 1333, Creating new feature extracting from existing 19311, Evaluation prediction and analysis 29987, Pre processing from 16764, Titles 12823, How are the Age spread for travellers 20441, bureau balance 23220, prediction with 5 time prediciting 27421, More num iterations 36590, Use all training data learning rate 3920, IQR 36635, We start with Na ve Bayes one of the most basic classifiers Here the Bayesian probability theorem is used to predict the classes with the na ve assumption that the features are independent In the sklearn library implementation Gaussian Na ve Bayes the likelihood of the features is Gaussian shaped and its parameters are calculated with the maximum likelihood method 4119, Optional Step Feature Engineering 25587, Function to transform model into model predicting log 31822, Random under sampling 5360, Diplay many types of plots in a single chart 642, we model a fare category Fare cat as an ordinal integer variable based on the logarithmic fare values 22439, Marginal Histogram 42545, Scatter plot of question pair character lengths where color indicates duplicates and the size the word share coefficient we ve calculated earlier 28854, Select One Time Series as an Example 42426, Floor Vs Price Doc 29011, Conclusions 13281, SVC is a similar to SVM method Its also builds on kernel functions but is appropriate for unsupervised learning Reference Wikipedia vector machineSupport vector clustering SVC 314, Embarked 35443, Previewing 7821, Plot learning curve 35112, Model DenseNet 121 29175, Apply boxcoxp Transformation to deal with features with values to all features with absolute skewness 8475, Model Hiperparametrization 4787, it is time to combine our train and test sets since we need to preprocess it the same way so that we can feed it later into our model 11014, we can drop Name attribute without any loss 35941, SVC 37525, Embarked Sex Pclass Survived 3836, Groping and Aggregation 730, Heuristic but effective 21684, Obten o dos dados 25990, Training Multi layer Perceptron Classifier 2764, Neighbourhood wise salesprice distribution 34076, Family Size 32925, let s add all the base features from the main loan table which don t need aggregation 7444, apply Gradient Boosting for regression and find the best parameter for GBR using GridSearchCV 24824, Output Prediction 6569, Since Embarked have only 2 missing value we are trying to fill with most common one 9181, Year Built 20086, Time Series Graph of Whole Company Sales 4648, Read Below for a quick learning note on how to combine multiple charts in Python using Seaborn 2128, With coefficients 15197, Title 23151, Findings Most of the passengers survived and died were from cabin But percentage wise its category B D and E that had impressive chance of survival People from cabin category X had just 30 chance of survival 3523, Finding Correlation coefficients between numeric features and SalePrice 27610, Create Data Generators 34830, Analyse and list the column with the null values 26205, We can delete width and height columns because we do not need them it can be easily pulled out from the images itself 12127, in the basement case we have some data missing in places where there s actually a basement but the vast majority of the NaN are really due to no basement in the house 17965, Easily select the string columns using the select dtypes Previously a column could only be selected by using its name explicitly 27835, random state in train test split ensures that the data is pseudo randomly divided 26857, Data Exploration 8744, New Features 21572, Clean Object column with mixed data using regex 27049, Visualizing Images with Malignant lesions 19607, Univariate analysis of categorical data 12333, MiscFeature 37359, Parch vs Survived 14523, Embarked 11610, fill them up one by one 29947, Random Search 34107, Hospitals in Urben and Rural Areas 3669, Box cox transform for skewd numerical data 13217, The plot between Gender and Target variable clearly suggest that more men have suffered the fate of Jack from Titanic 569, lightgbm LGBM 40872, Optimize Support Vector Machine 36632, there are outliers let s deal with that 9271, Dealing with missing values 14550, Age font 18488, Since associating 0 1 2 3 to categorical variables like StoreType Assortment StateHoliday affect the bias of the algorithm 17697, LOGISTIC REGRESSION 22956, Load test data 24114, Gradient Boosting 27229, Create XGB model and training it for intermediate values 23912, New Bayesian Optimization 39845, BASE MODEL 38625, Observe Training History 28820, From Monday peak of almost 10 to as low as 6 37690, give it a simpler task output 1 when image is digit 1 and output 0 otherwise 19813, Binary encoding creates fewer columns than one hot encoding It is more memory efficient It also reduces the chances of dimensionality problems with higher cardinality 39814, let s plot a random image along with it s label 20207, Converting cateogrical columns to Numeric 34680, Even though the volume is mostly determined by one set of categories the situation in case of revenue is slightly different 11071, Recover 1137, Model evaluation based on K fold cross validation using cross val score function 30907, One more step needed is converting the datatype from mixture to numerical datatype 33362, Visualization of outliers 9783, Ensemble Models 30068, Each data point consists of 784 values 24513, If we instead use a weighted random sampler with weights that are inverse of the counts of the labels we can get a relatively balanced distribution 13487, Check for imbalance class problem 18623, Converting Features 36996, explore now the orders 21809, Submission 39136, Output Layer 29949, Feature Importances 5650, Create a Fare category 39291, Discard irrelevant features 3146, Model Building 22971, Train Model 15352, we can try permutation importance 20126, Installed apps features 12715, Round 1 Initial models 32871, Build test set 21354, Compile Model 33153, Instead of summing up the income we sum up the actual values of our predictions 8349, Survival by Class and Embarked 33784, Column Types 9127, Electrical 13554, Geting the Fare Log 2444, Here is quick estimation of influence of categorical variable on SalePrice 20074, Insights 1414, FamilySize vs Survived 14474, so female survived more in than male since during disaster females are send from the ship first and siblings too 42204, Modeling Data 4581, we test a few potential variables 37140, Setting up Dataloaders 9228, Neural Network Classdefinition 15640, Feature Correlation 28373, Lemmatization 6171, Ticket 23447, Holiday 21192, Implement the forward propagation module 668, Gradient Boosting 8754, Data Columns 1714, Multivariate feature imputation Multivariate imputation by chained equations MICE 34742, Fitting a T SNE model to the dense embeddings and overlaying that with the target visuals we get 22597, Shops Items Cats features 12435, Lets deal with quasi constant features e features that are almost constant 14688, Model Creation 29525, Randomforest 3302, Utilities There is no NoSeWa in the test set basically no effect on SalePrice delete 20062, Load the dataset 26739, Plotting sales over the month for the 3 categories 10882, Examine Missing Values 7117, Make Submit prediction 4728, top 5 11673, It looks like passengers that embarked in Southampton were less likely to survive 23208, Findings Bagging can t beat our best base learners 4141, Evaluating the model 6213, XG Boosting 18196, cut products into 10 quantiles by summary adjusted demand 40837, Using the same pre processing functions on the test data 12970, It is very clear that distribution and median values of both men and females are almost similiar Therefore we cannot use gender variables directly for filling missing values 40726, Training Performance 9389, Feature BsmtUnfSF Unfinished square feet of basement area 26719, We have 10 National and Religious events 6 Cultural Events and 3 Sporting events in a year 28998, Since the distribution is right skewed we take the log transformation and convert it too Normal Distribution 6408, Null Values Treatment 20772, Quickly plotting a scatter plot of the first 3 dimensions of the latent semantic space just to get an initial feel for how the target variables are distributed 4336, Ticket 34365, Ideally I should have concatenated both the dataframes and done all manipluations at the same time 6271, Cabin 39015, Display death by embarked city 16113, Define training and testing set 9703, Normalization 85, Fare Feature 36837, Make predictions 14575, Overview the dataframes 20123, Prediction Submission 13724, FINAL CHECK OF CURRENT CORRELATION 32281, Display distribution of a multiple continuous variable 606, we start to use factorplots e 5375, Feature Importance 34521, Relationships 16968, after we cleaned and encoded the combined data set we split them to the original train and test data 15166, Random Forest Classifier 14534, Model Building 43342, Reshape the Data 24041, Submission 8783, One hot encoding for class 17997, To combine the two a feature named group size is derived by taking the maximum of the family size and the number co travelers of a passenger 9980, relationship between these countinues features and Sale Price 20539, Support Vector Regressor 618, A 60 yr old 3rd class passenger without family on board 16485, Embarked Ship 24459, let s use dataloader from this awesome kernel Melanoma Starter of shonenkov and vizualize some augmentations 30878, Some of the categories appear to have a higher variaiton per day than others 28412, Visualize 14887, Sex 24936, But to some extent it protects against outliers 17601, Logistic Regression 35320, Fit the model 6726, YrSold Vs SalePrice 14979, Filling Cabin missing values of test set 4901, do a little exploration on the training file 432, LotFrontage Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood we can fill in missing values by the median LotFrontage of the neighborhood 35060, Making predictions using Solution 2 11842, Lets look at all the Surface Area variables 19254, Daily sales by store 25050, Define Task 15431, let s one hot encode the passenger class 32862, Feature engineering 15129, Engineering Feature 24511, There is significantly less accuracy for the same set up 1019, And now we set our grid search algo 32302, Displays location of a continents 34740, Visualising T SNE applied to LSA reduced space by changing Perplexity 3592, Normalization 7913, Features engineering 3565, so we must use spearman correlation becuase categorical variables are mixed 34001, datetime 722, we got around the issue with NAs in the numerical data by replacing with the medians 24419, Home State Material 12405, For the first step it is always a good idea to start with analyzing target in this case the SalePrice 29939, Correlations for Bayesian Optimization 41168, Model configuration 20140, Implementation of Model 16826, Model Building 15389, Have a look at the training data 23817, 70 numerical features and 8 categorical features 33604, pull some of the digits from our training set 3716, Taking mode for all similar features like BsmtCond 24717, EigenVector 8 37634, After every epoch of training we compute the validation loss and the validation accuracy 5980, XGBClassifier 14405, FareBand 15826, first for unscaled data 19928, joining train and test set 29550, concatenate train and test datasets 20375, Get the best parameters 30645, K Nearest Neighbors 20319, Alright so we re resizing our images from 512x512 to 150x150 18631, Target Variables distribution 21681, I ve removed the real grid search parameters here because it takes too long to make it online 8081, Setup cross validation method 18466, quick glimpse at the data on hand 25913, Read orders 16678, look at all the columns in the data 7041, Lot configuration 12397, Reading test file 29037, Training set accuracy 10274, Lasso 15458, Pclass 21764, Zero Value for NaN in cod prov 32141, How to rank items in an array using numpy 6999, Types 1 and 2 finished square feet 962, Plotly Barplot of Average Feature Importances 7637, Stacking 11948, Transforming all the skewed values to log values to lessen the impact of outliers 25489, Training time 8889, we plan to remove the columns with a very low Standard Deviation in its values 11470, Decision Tree 29093, Functions for WRMSSE calculations 41117, An important note 34021, Count temp 23371, Above a scatterplot of test and train data for a very small XY window 6001, Impute data train dan test 40260, Garage Area 30112, We re going to use a pre trained model that is a model created by some one else to solve a different problem Instead of building a model from scratch to solve a similar problem we ll use a model trained on ImageNet million images and classes as a starting point The model is a Convolutional Neural Network CNN a type of Neural Network that builds state of the art models for computer vision We ll be learning all about CNNs during this course 41176, We initialize the weights of first two convolutions with imagenet weights 28423, How many outdated items in test set 8591, Filling missing Embarked values with Most Frequent with SimpleImputer 200, Library and Data 9831, In this case I use the age that was provided from our dataset to create the groups to find out if the passenger was a child youth adult etc 1812, Deleting features 34969, Random Forest Classification 256, Libraries and Data 14215, Bar Chart for Categorical Features 24004, Submission 32147, How to convert a PIL image to numpy array 3718, Handle Remaining missing values 28309, Identifying Missing Value Present in Application Train Dataset 30536, Exploration of Prev Application 40410, Looks like there are some outliers in this feature 9130, Set Pool Quality to 0 if there is no pool 4431, Is is clear by the graphs that SalePrice feature is skewed to the left 25912, Read train order products 23813, Those columns contain Geographic Information 24452, working with test dataset 43054, Density plots of features 7437, I build a neural network model in TensorFlow with two densely connected hidden layers and an output layer that returns a single continuous value 14094, Decision Tree 28184, To further clean our text data we ll also want to create a custom transformer for removing initial and end spaces and converting text into lower case 30839, Districts most vulnerable to a Crime 21637, Aggregating by multiple columns using agg 2453, Remove constant features 1036, Numerical features 43250, Saving data trained with logistic regression 20274, Correlation between categorical variables 26535, checkout the network a bit first 1365, I start looking the type and informations of the datasets 3434, While we re at it let s change the Sex variable so that male female is encoded 0 1 33583, Load saved model 17924, Importing train test split 3907, Kurtosis 13165, GridSearchCV for Hyperparameters 4563, Using BoxCox Transformation 20891, Inception CNN 1748, However there is a problem all passengers of Pclass and SibSp have missing values so we cannot find any median value for that category for simplicity sake we fill in manually the missing ages of those who belong to Pclass SibSP to be the median age of passengers belonging to Pclass SibSP value which is 5477, From the provided columns we have to select few columns as independent variables or features on which we can train our model 700, Furthermore we can check how well the target variables correspond with our predictions 474, Before we mvoe forward let s make a copy of the training set 43385, First of all we need to calculate the perturbations for each image in the test set 41102, Prediction using growth factor 1403, Missing values 23990, Fill the remaining columns as None 36593, LSTM models 20105, Item count mean by month item category for 1 lag 19835, Square root transformation 18882, Data imputation 26699, Lets look at the number of rows for each state 40302, There is some good difference between 0 and 1 class using the common unigram count variable 11733, let s plot the loss function measure on the training and validation sets 8306, Meta Classifier 18093, EfficientNetB5 456 456 3 15043, Cabin name is highly related to the Survived 4843, AS soon as we have numerical variables check for skeewnes and correct it 29228, Elastic Net 12154, Behind the scenes 5989, Simpan kolom Id 8019, It s Catergorical data 34448, Item HOBBIES priced at around dollars is the costliest item being sold at walmarts across California 6810, Random forest is a robust practical algorithm based on decision trees It outperforms almost always the two previous algorithm we saw If you want to find out more about this model here williamkoehrsen random forest simple explanation 377895a60d2d is a good start 10376, Handle categorical features 36863, Logistic Regression 15749, We may take Fare average Fare of passengers with 572, Second Voting 17051, Optimal parameters found during cross validation are lower than on public leaderboard meaning we are overfitting to training set increase n neighbors 9860, Total data table s variables value counts index 32470, kd Model 15623, Ticket feature 13856, Name 35447, Training 8771, Survival by fare 26740, Impact of Events and SNAP days on sales 36919, Age and Sex 2687, Lasso Method 38787, Apply probability frequency threshold and reset below threshold points to old values rounded 27210, my presumption was true and most benign cases are diagnosed as nevus 8317, Combining the processed numerical and categorical features 37333, Final model 6496, RoofMatl ClyTile 42688, REMIND repay 0 and not repay 1 23410, And now let s define the entire model 3644, Too many values for the Cabin feature are missing 18168, TPU or GPU detection 42012, Copying and creating a new DataFrame 28740, test set df test 42139, Encoder 6110, Garages and cars 27145, OverallQual Rates the overall material and finish of the house 23899, Seems tax amount is the most importanct variable followed by structure tax value dollar count and land tax value dollor count 22795, Lets us examine the shape of this dataset 12068, GrLivArea 28662, LandContour 38666, Logistic Regression 24810, Drop columns with too many 0 s 7751, I chose the features which are strongly corrolated with SalePrice as numerical attributed which be used in future model 31350, Usage 36292, Magic Weapon1 Scale our data and re train the model 19523, We can increase or decrease number of partitions with repartition method 41990, To read a cerain column 11132, Need to look at the y log relationship since that is what we be predicting in the model 15682, VotingClassifier Ensemble 4612, we look at the features that we deemed necessary to be tested earlier 34227, let s start building our ground truth data 9509, preview top 5 and bottom 5 records form test dataset 6596, Submission Back to Top 5120, Feature Engineering only for text fields 21503, look and the largest and the smallest images from both sets 7147, VISUAL EXPLORATORY DATA ANALYSIS 23617, Check category features of the dataset 29623, Feature engineering extract title from Name attribute 7255, Train model 24989, Ordinal encoding the categorical features before applying LASSO 30592, Missing Values 33354, Timeseries decomposition plots daily sales 42122, Model 11530, Elastic Net Regression 2928, In order not to slow down loading the kernel I commented on the parameter grids used by me and replaced them with parameter grids with the best models 9901, Sex 3724, Train Random Forest Regressor 41633, Removing Stopwords 26747, Sales data of each item at a weekly level 8455, Defining Categorical and Boolean Data as unit8 types 37623, We use the variable device to indicate whether to use GPU or CPU 14862, Excellent we have seperated the passengers between female male and child This be important later on beacuse of the famous Women and children first policy 32155, How to create a numpy array sequence given only the starting point length and the step 22486, Bonus2 how to make create hyperlinks inside a document and change the color of a text 29830, RNN 17050, KNN 20259, Assume we have equation y x 2 3581, And treats the missing values as 0 like BsmtFinSF1 variable 23592, Merge predictions 36923, Chances for Survival by Port Of Embarkation 10622, Looking at missing amount of data from Cabin 25266, Croping Images randomly for resizing 19326, Countplot for each of the 10 digits 4342, Summing up number of parents children and siblings across passengers 42339, Make Predictions 9403, We easily plot the OLS regression line as follows using the python code 26760, Preprocess 20544, Light Gradient Boosting Regressor 6065, Decks porch fence 3280, Model Building 33022, The standard deviation is much lower now good job br 38201, Baian optimization works by constructing a posterior distribution of functions that best describes the function you want to optimize 6876, XgBoost Regressor 40193, Visualizing data w r t each class 757, Simple Linear Classifier 36741, Make Predictions with Validation data 35082, Ploting the relation between the Variance and the Dimensions 14809, Find Missing Value 30837, Extracting Feature from the Address Feature 22932, For sex there is not much we can probe from 42019, Creating a new Dataframe with exisiting rows that matches a certain condition 42571, These functions are used to calculate the performance of our predictions at this competitions metric the logarithmic loss 1029, Example of categorical features Neighborhood 22339, Text to Numeric Conversion 42410, Fill missing values 37024, How many different brands do we have 24680, Model Evaluation and Prediction 8564, Handling Missing Values 3986, Processing categoricals variables 21201, Visualizing and Analysis 30403, Train the model using data augmentation 22623, Define Network Architecture 3987, Make mean target encoding for categorical feature 42367, Modelling 39089, Prediction 43300, Cross Validation Feature Importance 41524, Scatter Plots 3810, Light GBM 3649, Grouping fares in Fare feature and assigning values based on their survival rate 8675, Importing the Modules and Loading the Dataset 16820, People from Cherbourg have more survial than Deaths 35861, rd Training Last 2302, Pandas Fill in NA s 8832, Splitting the dataset 35055, Accuracy gotten after training 20493, Test Image Embeddings 42171, View the corresponding labels 25744, Removing shape information 4556, LotFrontage Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood we can fill in missing values by the median LotFrontage of the neighborhood 5841, Checking Feature data distribution 34792, Linear Regression 15039, Does the Fare related to the Survived 42819, Creating Dataset 25047, Add to Cart Reorder ratio 22833, perform a similar train test analysis for item ids 15301, XGBoost Model 40963, Checking out other models Uncomment and Run 37854, Exploratory Data Analysis 31517, We determine the shape of the matrix and confirm if the shape of rows for the input matrix X is same as that of y 16889, Fare vs Survival 20923, Submit 20528, Imputing missing values 21349, Accumulate the image names 28814, Saving our Model 4260, Dataset Statistics 22331, Convert Emoji Into Words 2998, Creating Dummy Variables 9210, Distribution of Ticket Class to Survival 32181, Define optimizers 38288, MasVnrType and MasVnrArea not yet imputed completely 14335, Droping few column which doesn t useful for prediction 5670, Fill basic missing values for Fare feature 18563, size 7 34003, holiday 34709, Mean over all shops and all items 36753, Importing Various Modules 1654, Now let s have a look at Age 17916, ANALYSIS OF GENDER AND SURVIVAL STATUS 1678, Analysis of a numerical feature AnalysisofaNumericalFeature 5141, Categorical variables 2934, Apply the model on test dataset and submit on Kaggle 8583, Applying RandomForest 14657, The Married status and Pclass are important in determining the age of the person 16052, From which location passenger go on board to Titanic does it matter or its more important that passenger is on Titanic no matter from where you go on board to Titanic as we know that At 2 20 a m on April 15 1912 the British ocean liner Titanic sinks into the North Atlantic Ocean 15311, Lets find out the Survival Rate 36382, CNN 16918, Confirm no missing data 9275, XG Boost model 11740, Just as expected the graph looks very similar to the first one we looked at 9014, Set Fence Quality to 0 if there is no fence 40948, Great 28858, Difference Transform 43320, predicting the values with the model 37218, First does the embeddings index contain digits Google News replaces numbers 9 with signs 151, Gaussian Process Classifier 21592, Remove a column and store it as a separate series 24440, LightGBM 32683, DataArgumentation 20124, Training Loop 9974, Heatmap 37417, We only need a few prinicipal components to account for the majority of variance in the data 26949, OBS 30 CNT SOCIAL CIRCLE 22491, Bonus7 Sankey plot in Python 27537, Display the distribution of a continous variable 6164, Age 43134, Fatalities 3 Worst Predicted 86, Here We can take the average of the Fare column to fill in the NaN value However for the sake of learning and practicing we try something else We can take the average of the values wherePclass is 3 Sex is male and Embarked is S 6524, Since the numeric variables are skewed we perform log normal distribution 1720, Either train 8577, Machine learning algorithms cannot run with categorical columns so we need to make them numerical 5368, Permutation Feature Importance 5556, Make Predictions 15059, Most Correlation 161, Our first step is to load and quickly explore our train dataset the previous cell output helps us to find the file names To read the dataset we use the read csv Pandas method Now the previous listing of our files comes in handy since we know the input to our method 22697, Lets Do CrossValidation 43382, Training the model 12399, Removal of null values 22834, There are a lot many more items in the training set than there are in the test set 35922, Define the model using Keras and TensorFlow backend 496, I think the thought here is that individuals with recorded cabin numbers are of higher financial class and in this manner bound to survive 31771, Pre trained model preparation 22942, with the numbers there were several glithces in our algorithm 41195, check if there are any categorical columns left with missing values 42534, No need fot the date column now so dropping it 28300, Have a look some perfomance metrics on validation score 13515, Section 2 Modeling 24008, Train and validation 39674, We define our error as the squared difference between our true Y and our predicted which gives us an absolute error metric 22528, Dropping columns and filling NA NaN values using the specified method 34763, Fitting Model on Total data 1747, Fill in missing values for Age 19431, Check NaN count in each column 18066, Logistic Regression Model 16893, New Feature haveCabin 207, Confusion Matrix 8055, submit 41790, Loss and accuracy 30136, Input are 2 Numpy array Let me briefly go over them 32591, Learning Rate Distribution 4373, we can generate one new feature using these all bathoom related features 30595, We need to align the testing and training dataframes which means matching up the columns so they have the exact same columns 30004, test the model 11213, LightGBM 37621, score3 600 3826, We might require a sample size greater than 50 6217, GarageType Garage location 7804, Blend Models 24104, Applying Machine Learning algorithms 5376, A Quik View of Data 36475, MissxMarisa is the most active users with 23 tweets followed by ChineseLearn erkagarcia and MiDesfileNegro 37301, Unigram 9712, try to interpret the coefficients 3587, Frequency Encoding 8588, Filling missing Age values with Median with SimpleImputer 15337, The Data that we are dropping from the dataset 11194, Additional testing 8462, Prepare Data to Select Features 29022, save the target column separately 6747, Multivariate Analysis 24932, Some tasks may require additional calendar features 3441, check the ages of the passengers with the female honorifics in Title Lady and the Countess 881, all data for submission 29924, In both search methods the gbdt and dart do much better than goss 16649, Overview of The Data 18495, A crucial validation 35687, LightBGM Hyperparameter tuning 20509, import the libraries 19058, Viewing a batch 6708, All columns have more than 1 unique value No feature found with one value 11086, make sure to optimize parameters once in the start and then once again in the end after all your data tweaks 39383, Label encode all categorical features 25216, Impute the numerical features and replace with a value of zero 43367, Viewing some example 30462, Preprocessing pipeline 22812, Dealing with Outliers 41165, Imports 4432, Way better 1896, RandomForest Model 1542, Embarked Feature 27294, Intervention by Hill function for SEIR model 29425, use the sample submission csv file as reference and fill the target column with our predictions 37169, practise on a simple decision tree 6374, Find out the mean value 13589, Calculating the class weights 10009, MSSubClass Na most likely means No building class We can replace missing values with None 3591, Make a Categorical Variable 1 if exist 0 if absent 26678, Numerical features by label 8983, How do these categories affect price 32992, ROC curve 37052, Feature Engineering Or Data Preprocessing 9378, Ooh 29866, The dataset is highly imbalanced with many samples for level 0 and very little for the rest of the levels 995, And now let s convert our categorical feeatures to a model ready type 19159, Submission 8118, SVM 10668, Assign model 17258, Describing training dataset 29014, Distribution of Age feature by Survived 40407, Bathrooms 22147, GIVING WEIGHTS TO EACH FEATURE S RESULT CV 22906, Findings 36062, Scale Data 35953, Filling missing values Age fare and Embarked Cabin have missing values we ll take different approach to fill them but we ll drop the Cabin feature because it s usually advised to drop feature with high amount of missing values 50 20236, Fare 23644, Model 1384, Anatomy of a neural network 43091, check how many features we have now 6677, Bagged DecisionTree 30779, Using test csv to make another prediction 42902, MCA Factor Map Biplot It s a global pattern within the data Individual categories and variables 30317, If more than one candidates are available we here take only the first candidate and discard the rest for plainness 41224, visualize one row of the train dataset 39912, As Kaggle kernels have 1200 seconds limit I have divided the prediction step 43215, Import libraries and fit the model 6734, TotalBsmtSF Vs SalePrice 34348, now let s read in the test data and generate our submission file 42452, Ordinal variables 43114, Use the next code cell to one hot encode the data in X train and X valid 865, Embarked and Sex 4876, Lasso 39113, Parch 22044, Since schools generally play an important role in house hunting let us create some variables around school 20669, We changed the type of MSSubClass to string so we can impute the median based on MSSubClass in MSZoning in the next step 35756, Keras earning rate and early stopping during training 24726, After creating new features we can drop useless columns that we won t use in the training process 14292, Start Diving into it 20734, Heating column 14584, There are no missing data in the Test information 5410, Well I insist if we input thise feature without any preprocessing it cause over fitting since people with 5 and 8 SibSp all survived but there aren t many of them 32816, Macro economic factors influence on House pricing in Moscow 11655, Find better hyper parameters 32967, Passanger Class 8994, Missing values 15173, Fare 8615, Here i have printed top 20 features which make huge contrubution to target variables and now i check whether any of these variables have null values or not if there are any i fill them with most common values 15461, Ticket Number Remap 41653, Retrieval 18927, Relationship between numerical values 41289, Using the saved weights in a new model and checking predictions 41961, The words need to be encoded as integers or floating point values for use as input to a machine learning algorithm called feature extraction 29180, Trick Combine BsmtFinSF1 BsmtFinSF2 1stFlrSF and 2ndFlrSF 1768, Cabin 34234, For our DataLoaders we re going to want to use a ImageBlock for our input and the BBoxBlock and BBoxLblBlock for our outputs our custom get items along with some get y s 28777, In the previous versions of this notebook I used Number of words in selected text and main text Length of words in text and selected as main meta features but in the context of this competition where we have to predict selected text which is a subset of text more useful features to generate would be 40405, idling is not me 26906, Score for A10 14810 42135, Apply model to test set and output predictions 37937, Before we start let s take a quick look at the data structure 23597, Define a tokenizer 14960, Impute missing embarked value 26788, Clustering NYC data 36429, Train Test Split 15218, Model CatBoost 2212, General Scores 20546, Base Models Scores 43267, Desenha a rvore de decis o 13857, We can replace many titles with a more common name or classify them as Rare 7085, People with high Fare are more likely to survive though most Fare are under 100 42855, Top words No disaster tweets 36397, EDA with describe 20731, Foundation column 24831, Here you can choose to run the subpart 6333, But before going any futher we start by cleaning the data from missing values I set the threshold to 80 red line all columns with more than 80 missing values be dropped 26855, F Score Graph 31430, Apply fresh JEL 26011, Just to try out as a traditional approach I am going to run a Correlation Heat Map for all the variables 20414, Here are some questions have only one single words 41433, From the pricing point of view all the categories are pretty well distributed with no category with an extraordinary pricing point 4988, Tickets 5369, Recurrent Feature Elimination 36336, Before the model is ready for training it needs a few more settings 24985, Removing numeric variables that have low correlation with target 11664, Adaboost 24737, Agglomerative Hierarchical Clustering 5956, Correlation Matrix and Heatmap 21619, Store NaN in an integer type with Int64 not int64 23367, Analysis after Model Training 32918, Try my model on random images 1 Dog 0 Cat 1941, nd Floor with SalePrice 1016, And let s print our accuracy estimated by our k Fold scheme 11750, Feature Engineering 15743, split data 11700, Experiment 2 Compare the custom BCE and TF built in BCE 26679, EXT SOURCE 2 14093, KNN Output File 41456, Feature Sex 39332, Text features for items df 25398, Submision 30858, Implementing a max pooling layer in TensorFlow is quite easy 27606, With the cleaned image segmentation is performed thereby dividing the image into four distinct regions background wall left lung cavity right lung cavity 13098, Modifying seaborn countplot make it work with FacetGrid when all 3 arguments are used 3286, Train models again 12139, LGBMRegressor 2 6523, Temporal Variables 39183, Visualiza es 5508, Dropping Columns 36815, then we ll define the test and training data URLs to variables as well as filenames for each of those datasets 589, Load Python modules The list of modules grows step by step by adding new functionality that is useful for this project A module could be defined further down once it is needed but I prefer to have them all in one place to keep an overview 7392, For the final dataset I drop the surname and name codes because they aren t needed anymore 18036, Boy 16218, Feature Engineering 7050, Exterior covering on house 18156, Normalize the Sparse Features 39693, Remove Stop Words or and Frequent words Rare words 10931, Importing Pythons Moduls 29554, If we want to use torchtext we should save train test and validation datasets into separated files 27994, DecisionTreeClassifier 27895, Building the ConvNet Model 40783, because you changed the cost you have to change backward propagation as well All the gradients have to be computed with respect to this new cost 5563, Moment of truth 20711, Data is highly skewed 30924, Add our predictions along the original data 39401, Numeric Features Exploration 23757, Analysis of Temperature and Confrimed Cases via Plotly Graphs 40616, I created a quick imitation of test train split in order to compare of OOF ensembling methods 32005, Observations 29316, You have a higher chance of surviving if you have a first class ticket than having a second or third 99, Passenger who traveled in big groups with parents children had less survival rate than other passengers 1169, there are houses with garages that are detached but that have NaN s for all other Garage variables 35201, In previous figures we mentioned about visualizing proof later 28986, Missing Value Analysis 23570, Visualize the model s training progress using the stats stored in the history object 6824, we ll add XGB and LightGBM to the StackedRegressor 30926, Question texts are not printed fully but shortened Lets fix that 41929, XGBoost Starter 18663, Make Submission 37453, we need to make each output of the same length to feed it to the neural network 29787, Sample few test images 21361, Preparing the data and the model 27129, Sale Price 16448, Embarked 42606, Model 33778, Fitting the model 8666, CentralAir and Street is a binary features so we can encoding ones with 0 and 1 9797, After all the processes we can divide the training and test set from each other for model testing 32625, Transforming tokens to a vector 36470, Images from University of Saskatchewan 3529, Box plot Neighborhood 19339, Reference Table 5387, Compare and Compound Classificaton Algorithms 26032, Preprocessing The Data 7605, for numerical features 5225, PCA 7901, Bulding the model for the test set 5977, Grid Search on Logistic Regression 34066, Pclass Survived 33677, Random font 33095, AdaBoostRegressor 35108, Quadratic Weighted Kappa 14892, Apply the correlation Heatmap 16396, Calculating Percentage 10346, Skewness and Kurtosis 8650, get a view of the actual data 8106, Sex 5690, Use Feature Importance to drop features that may not add value 9100, How does an unfinished floor affect the SalePrice 3002, Train Score is 0 26471, We use training set as our data that be further split into train test due to memory constraint 4801, For all the numeric features we have checked for skew and have log transformed those features which have high skew greater than in our case to get a normal distribtuion 27254, Create a Dict data type to hold the parameters for our First Level Models 13847, But before going any further we start by cleaning the data from missing values 35341, MISCLASSIFIED PET IMAGES 30411, Train and predict 34283, checking for improvements in count over time 16036, Here is the public score so far 11744, analyze which variables are missing 37878, Top 10 Feature Importance Positive and Negative Role 4409, Indicator dummy Variables 38494, let s take look into the distribution of the target 893, Train again for all data and submit 30177, I thought of a sigmoidal function because China s data resembled a sigmoidal shape 28202, Named Entity Recognition 6361, The Lasso regression 28466, Column yearbuilt 8816, from Above information we can fill Age Information with 18003, Irrespective of whether we process the training and test set together after we decide on the hyperparameters we use the entire training set to retrain the model and then use this model to predict the test cases 42242, Missing null values in numerical columns 3488, Classification report 12495, Building final model with best parameters and calculating on full train data with voting regressor 30355, Predict World With China Data 12276, Import required packages for Feature Selection Process 31228, Features with positive values and maximum value greater than 20 10463, let s visualize an estimation of the probability density function to get a better understanding of how values of each attribtue look like 7930, Lasso model 35569, Visualizing the data for a single item 27226, Using ImageDataGenerator to bring in augmentaion in data 4517, Linear Regression 9735, Appendix 9362, Completing a categorical feature 13322, Observations 28168, Just in case you wish to disable the pipeline components and keep only the tokenizer up and running then you can use the code below to disable the pipeline components 23949, if the percentage is greater than 0 7095, Models include 10756, create a new feature 22112, step is guess what new feachers we need to intoduse to make the model better 21512, Padding images 33992, RandomForest Regressor 553, SVC RandomizedSearchCV 22452, Dumbbell plot 12427, Using map 24549, In case of total products 1 20537, Linear Regression with Lasso regularization L1 penalty 27197, 3rd Step Data Analysis and Some Visiuality 4182, Fare 19824, Information Value 35633, Histogram of L 2 distances from the eight digit 40155, Apperently this information is simply missing from the data 25353, Locations 24981, Extracting categorical and numeric columns 1291, Hyper Parameters Tuning 5 3 19762, Application of the cleanUp function 26744, Plotting sales of weekends before different event types 33491, Albania 18707, let s download the file and upload it to a bucket in Google Cloud Storage 34671, Average sales volume 42277, Check NA 3606, Another way to look at null values using a trick from 24126, Looks like some values from Keyword and Location columns are missing let s explore these columns 10747, Check missing values in features and impute 35070, I try again but this time I add a Lambda layer at the input of my NN 40259, Garage Cars 29426, fill the target column 15910, Age and Sex 41743, Error analysis 742, Final DFs 14966, Though there are more males on the ship but females survival chances are more than males 15673, xgboost 21204, Parameters Initialization 19594, main cate id 38035, Ensemble Learning 34907, Fill it 274, Model 6176, More number of Males are present then comes Females according to titles 5588, discretize Age feature 6076, Embarked 7981, We should drop from orange blocks 34714, Mean over all months 27651, We perform a grayscale normalization to reduce the effect of illumination s differences 4521, Using Neural Nets 11843, Creating a TotalSF variable from TotalBsmtSF and 2ndFlrSF 37611, Inorder to fill the missing values effectively you have to understand the data in the dataset Go through the data description 18083, The brightness of the images 4557, MasVnrArea NA most likely means no masonry veneer for these houses 15946, AgeGroup 21452, Numercial 16554, Dropping the unnecessary features 1296, Univariate Analysis 35931, Age Fare 14596, Ticket class 33795, Effect of Age on Repayment 16161, SVM 1071, visualization 13423, Baseline models 11318, Fare 38695, After Mean Encoding 31734, Save the files before continuing 23221, prediction with 1 time of prediction 40977, Another way to look at the data with groupby 3637, Merging Titles 32328, transaction date 28279, Generating the submission file 34920, Words length stats in tweet 10427, Showing Confusion Matrices 43122, Checking correlation between features 35810, Add test 11457, Age and Sex 15909, Further exploring the relationship between Pclass Sex Age and Survival 6425, PCA Dimensionality reduction 15679, Plot Area under ROC 12313, GarageArea is divided into 0 and non zero parts 13564, Mapping the titles 12812, now lets check missing values for test data 32193, We ll remove the obvious outliers in the dataset the items that sold more than 1000 in one day and the item with price greater than 300 000 3935, The rest I just delete because those are correlated or not so important 26688, MEAN BUILDING SCORE TOTAL the sum of all building AVG score 12189, and we want the data to be scaled before going to our model 19161, if category present yes no features 41302, How many categorical predictors are there 40382, format path train and format path test merely takes in an image name and returns the path to the image 30775, Using KFold for cross validate 30080, Cleaning Data 6424, Data Scaling 26752, vil 23694, For some reason uusin Matthews Correlation 4248, Sherpa 29254, based on that and the definitions of each variable I fill the empty strings either with the most common value or create an unknown category based on what I think makes more sense 9792, Encoding nominal values using dummies 23538, For test data we have 28 000 entries 37279, Our functions for loading our embeddings 19054, Getting some quick batch tfms 11917, Embarked Column 7018, Evaluates the height of the basement 15779, Logistic Regression 18025, XGBoost model 38544, To visualize the tree we use graphviz 1028, Interesting The overall quality the living area basement area garage cars and garage area have the highest correlation values with the sale price which is logical better quality and bigger area Higher price 36547, take a look at n top features of your choice 23922, Prediction and submission 8380, It s time to go 14805, Basic Data Analysis 23841, Taking X10 and X54 2333, Imbalanced Dependent Variable 33019, Preprocessing 32607, Numeric Features Exploration 20837, We ll do this for two more fields 8060, Various datatypes columns 10947, merge train and test data set 20851, In time series data cross validation is not random 42051, Change string to numbers by replacing values 16905, Conclusion 15784, Choose best model 39843, Quater Wise Resampling the data 21374, Reshape 36262, Most Important thing when plotting histograms Arrange Number of Bins 39667, Splitting chosen image into inputs and targets 25052, Find best coefficients 37806, Elastic Net 34911, Count len of tweets 4995, Poking around 20059, Optuna is an automatic hyperparameter optimization software framework particularly designed for machine learning 26840, Rides Per Year 43157, Convert the data 17411, calculate the mean of all the feature importances and store it as a new column in the feature importance dataframe 7761, Preparing Train Data for Prediction 21594, Read data from a PDF tabula py 41866, Below there is a cloud of words from alphabetical entities only in a shape of fire as well 7771, Random Forest 33713, FEATURE AGE 20825, In pandas you can add new columns to a dataframe by simply defining it 7315, From following cells we could know that train and test data are split by PassengerId 13570, Creating the Family Size feature 22366, Create Column FamilySurvived FamilyDied 39414, Embarked 26379, Extract bounding box data 9041, drop these columns 14124, Age 29047, Removing the circular boundary to remove irregularities 37154, Now we choose a particular image which we use in the later sub section to view the features learnt by the model Click here to go to that section 15957, Test set 33074, Data preprocessing 24765, Gradient Boosting 1201, Elastic Net L1 and L2 penalty 18816, LASSO Regression 22465, Pie chart 2573, Model and Accuracy 16027, Cabin 15644, Split data into test and training 42014, Creating a new column by copying an existing column 32111, How to print the full numpy array without truncating 15078, PCA 8154, Valuation Models 11919, One Hot Encoding 158, EDA of training set 909, We transform the Saleprice in log transformation for more clear linear relationship 31514, Satisfied customers 4347, By Observing the house feature can conclude that the house s Salesprice is low with respect to all of its features 36540, Test 16337, k Nearest Neighbors 701, The first thing to notice is that this dataset is that there are a fair few variables 24673, MODEL TRAINING 17420, as it is a classification model we have so many ways to fit it but lets chose the best one 14008, Ensemble RF LR KNN by voting 7269, It s clear that the majority of people embarked in Southampton 29223, we dummy coding the remaining categorical variables 10338, First of all let s start by replacing the missing values in both the training and the test set we be combining both datasets into one dataset 24970, Model Inference 2913, ok after several tests I decided how to do the feature extraction I wrote a function that encapsulates all the others I used for this purpose 10, Pipelines 38945, Plan of action 5433, Pool 28449, COLUMNS WITH NO MISSING VALUES 23698, Make predictions and submit 31365, vectorizing 17981, Prediction 5924, The data is right skewed 24591, TRAINING THE MODEL 22335, If you want to know what exactly each tag specify use nltk help function as below 17024, Hypothesis There is some correlation in title with survival rate 32236, let s train our model several times depending on what we set epochs to be 2814, combinion the traning and testing dataset to maintain consistency between the sets 42048, Lower case the column head 14433, go to top of document top 7762, Cross Validation 11754, First we are going to split all of our data back into our train and test data sets 64, predictions 28102, Separte the Categorical and numerical columns 11725, Bagging classifier 20548, See the loss 4256, Nulls in testing set 6875, Linear Regression 12374, List cate It is the list of all the categorical data fields in the dataset 29968, Collecting texts for training corpus 13544, Exploring the data 7513, Prediction and submit 19090, Correlation 16075, ROC AUC Curve 6313, we have found the optimal feature set for each of the selected models This is a very powerful exercise and it is important to refine the dimensionality of the data for each individual model as each estimator work with the data differently and hence produce different importances 39717, Exploring the trained Word2Vec model 34264, To use the day of the week we merge data from the calendar DF 7325, By fixing the values of Embarked and Pclass we could plot histogram of Fare And we should use the most common value to replace the NA value of Fare 3853, Feature AgeState 5889, Same indices are missing it means that if area 0 those coln are missing 32781, Scheduler 18926, Data 6060, 1stFlrSF well distributed a couple of outliers 16384, S 0 C 1 Q 2 13102, One hot Encoding and Standardization 23153, Findings 93 passengers died with ticket category A over 64 survived from category P Over 57 survived from F and just over 15 passengers survived from ticket category W 26652, check if ad id can be used as index 38249, One might ask how is this helpful 6463, Pipeline for the numerical attributes 22209, Here is the commented out version for when I was working with 2 models instead of 3 25810, Evaluate Model 15562, we are ready for the real filling of all missing ages 23329, visualize what the light pattern looks like on the the photodetector array 30311, Import modules 8788, We use cross validation to evaluate your model 25853, Some other symbols 35384, SUPPORT VECTOR MACHINES 24261, Descriptive statistics 20932, Visualize the training history 29350, Babies are more likely to survive than any other age group 43259, Criando a coluna rolling temp 20346, and also plot some of the training images 9060, It is confusing to me that the minimum of this is 2 14722, let s look at the distribution of titles for survivors vs 29559, Predictions 16387, Combining Titles and Sex only 24746, Exploration 17417, sibsp of siblings spouses aboard the Titanic 35913, Compile the model 37547, PYTORCH MODEL 23809, Preprocess Pipeline 20442, credit card balance 32753, Function to Aggregate Stats at the Client Level 2644, Fill NaN or null values in data 12816, Gender Analysis 32509, Loading the weights 14364, Feature Desciption 29430, Make Predictions 6618, Function for Evaluation 22444, Diverging lines with text 22537, Creating the submission file 31234, Features with max value more than 20 and min values between 10 and 20 2757, Dropping the columns that have more than 30 of missing values 15250, Tune 2463, ANOVA F value For Feature Selection 11722, Passive Aggressive Classifier 15028, In the speculation when we explore the feature Sex we think that the dead ratio of Adult Male be much higher than Adult Female and Children 31425, Case x start x end 8155, Submission 5498, XGBoost Regressor 22600, Price trend for the last six months 15401, It s a 3rd class passenger so let s fill the missing value with the mean fare for passengers of the same class 28037, Lets remove variables for having clean workspace for rest of the kernel 25508, CREATING SAMPLE CORPUS OF DOCUMENTS ie TEXTS 27338, One Hot Encoding 25803, Before continuing let s give them some fake identities based on the most common first and last names in the US 18653, Exploring categorical fields 31894, Performing 5 fold C 16276, Defaults 31509, We create two dataframes 31724, EDA take a look 37191, Feature Engineering 1604, Conclusion 6845, Checking the Imbalance of Target Variable 11062, Embarkment versus survival 19060, Specify the architecure to be used 8729, Correlations 8039, Add new feature 40964, Saving the Submission File 12449, With the GarageYrBlt my first assumption is that it must be strongly correlated with the YearBuilt 22425, This plot is an example of the power of matplotlib By the end of this kernel you learn to do this and more advanced plots span 5692, Predict the output 8594, df train cat is a sparse matrix to convert it to dense matric use toarray 20768, applying these functions sequentially to the question text field 42660, The difference between the two matrices is sparse except for three specific feature combinations 43256, Mudando o Nome da Coluna count para rentals 1898, KNeighbors Model 1865, Compare Models 28036, CONCLUSION ON TRADITIONAL METHODS 37679, Preparing the T5 Dataset 10773, First thing first we split back the data into training and testing set and we drop some non used feature 4703, I defined this little and very helpfull function to compute the Root mean square error 18611, Model 1 SVM Linear Accuracy 79 28417, Simple bar plot of the feature importances to illustrate the disparities in the relative significance of the variables 19373, Transforming data to reduce skew 285, Sex 4795, log transform it and recheck 17980, Applying Classifier using sklearn wrapper 9383, fill the features of this house with appropriate values 28021, LoggicReg 8575, Concatenating DataFrames 13336, let s convert the title feature which contain strings to numerical values 13148, Family size as Survival Function 8818, IT S CLEAR ready for feature engineering but we ll drop Ticket first 3604, Independent variables 436, BsmtQual BsmtCond BsmtExposure BsmtFinType1 and BsmtFinType2 For all these categorical basement related features NaN means that there is no basement 14606, Naives Bayes 24866, Data Analysis 43005, Data cleanning drop duplicate columns 3420, Extracting Combining Ticket Prefixes 37617, XGB Regressor 17730, The data reveals that some passangers had more than one cabin 10612, Selecting best method of imputation 25035, Seems like 0 and 1 is Saturday and Sunday when the orders are high and low during Wednesday 11626, Model building 36213, Exporting the different submissions 22820, Nope The outlier is the only transaction 15613, Title Cetegory 14293, Reading the dataset 36450, Callback 14511, Observations 38166, For TPOT everything needs to be in float or int therefore deleting variables that are not those for example purpose 4197, Feature engineering 1539, that we ve filled in the missing values at least somewhat accurately it s time to map each age group to a numerical value 498, Fare Feature 14494, Random forest accuracy increased to 17404, kf means K Folds cross validator so kf KFold generates a K Folds cross validator which provides train test indices to split data in train test sets 35574, Rollout of items being sold 4585, BsmtFinType SF is more powerful than its creaters in the case of type 1 but not so in type 2 this is strange as we expect the behavior would be consistent 14144, AdaBoost Classifier 2903, Stacked Regressor 36090, Histogram of successes and failure rates 38849, Family houses selling price is too high 6156, Stacking on test subset 7962, display the columns with the number of missing values 11898, This is a general method in handling outliers trend 11905, Ensemble and submission 37694, error is smaller measure accuracy and f1 6819, There are some outliers in this data as well but we are not going to remove these ones too because we don t want our data biased and our models affected by that bias 26002, step is to split up the enc train into a training and validation set so that we there s a way to access the model performance without touching the test data 40718, Model Using Keras 16699, do this for all the other classes as well using iteration 3623, First let s get a baseline for where we are without doing too much more clean up work 30853, Locations For Assault 8551, Other Numeric Features 542, Fill NaN with mean or mode 14572, Output font 37040, This Keras function simplifies augmentation e 2250, Getting the Data 10859, Checking the correlation of the numerical features 16452, Pclass 19695, Define Optimizer 40457, OverallQualNCond 42335, Here summary of the model can be interpreted with each layer separetly containing 14854, Plotting the survival rate by family size it is clear that people who were alone had a lower chance of surviving than families up to 4 components while the survival rate drops for bigger families and ultimately becomes zero for very large ones 40267, Lets create new Binary Columns for Basement Garage so we can analyze there distribution as well 1191, Overfitting prevention 3393, Feature Selection with Correlation 13341, Cabin extracting information from this feature and converting it to numerical values div 6674, In ensemble algorithms bagging methods form a class of algorithms which build several instances of a black box estimator on random subsets of the original training set and then aggregate their individual predictions to form a final prediction 17759, Clean Data 36083, Training 31050, Specific String Rows 3153, the error metric RMSE on the log of the sale prices 6125, Comfort houses by the way 33615, How many of each digits mis classified 6286, I separate the predictor features of the training dataset from the Survived column which is what we want to predict 37766, That method is not recommended because we can easily overlook a variable that should be removed or remove a used variable by mistake 33043, Neural Network using nnet 24508, New distribution 6221, dereferencing numerical and categorical columns 41329, After compressing the 784 pixel features to 50 features we train the t SNE algorithm 1560, Upon first glance the relationship between age and survival appears to be a murky one at best 41341, Detect Delete Outliers 12611, replace missing values by median of Age 16770, A general rule is that the more features you have the more likely your model suffer from overfitting and vice versa 9004, check for duplicate rows if there are any 2109, Moreover we have already transformed some categorical features into ordinal ones 31838, Creating the custom metric 28999, Scaling the data 14932, Roc Curve 15828, Logistic Regression 1732, Plot Embarked against Survived 42780, Turning labels into 1 hot encoding transforms the shape from 1 column to 10 columns 4885, Fix data 31798, Real or Spurious Features 31284, ARIMA 4341, Test dataset 33708, LOADING DATASET 8798, combine both train and test data to save our time and energy 34523, visualize one of these new variables 40473, Final Column Drop 42246, Dealing with missing null values 12935, PassengerId 62 and 830 have missing embarked values Both have Passenger class 1 and fare 80 11344, Fare Binning 20278, center center 22253, SVM 23885, Univariate Analysis 8164, This is pretty straight forward as we onehot encode the input variables before splitting up training and test set and scaling each individually 32482, The final piece of the puzzle is an evaluation metric to ascertain the performance of each model 12661, The dataset contains a lot of missing values 42351, Assumption is mostly correct 8659, Instructions 1026, have a look first at the correlation between numerical features and the target SalePrice in order to have a first idea of the connections between features 15343, Lets try using Random Forest A Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap Aggregation commonly known as bagging The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees 16596, Predict the outcome on Test data 31706, Cross Validation 41753, You have some problems with employment but your external scores and annuity to income ratio is good so we can give you a credit 13211, Our tree are very extensive and probably our model are overfited let s create a submission with test predictions and upload on kaggle to confirm 28242, Generating Ground truth 23405, Labels and Features are splitted 39879, right bottom two out liars 17524, Prepare submission file 20283, P1class survival chance is around 63 13077, we deal with categorical data using dummy variables 32414, Convolutional Neural Network 23274, Sex 15916, After Adjusting the surnames of families group these true families together again 24895, To score higher in kaggle competitions it usually is the case that final model is derived out of multiple previous models 782, Lasso 25018, Our plot function takes a threshold argument which we can use to plot certain structures such as all tissue or only the bones 2130, Tuning Ridge 7, focus on some of the categorical features with fewer missing values and replace them with the most frequently occured value which is the mode of that feature 25237, Sklearn Classifier Showdown 19544, Tokenization 37644, Find position and neighbourhood from latitude and longitude 14350, How many Passengers Survived 23832, But YOU MUST SEE THAT SOME FEATURES ARE HAVING SAME CORRELATIONS THAT COULD INDICATE THE POSSIBLE DUPLICATE FEATURES Lets check them too 2992, Feature Engineering 22947, I also isolate the number value of the Cabin feature 4480, We first create these functions to allow us the ease of computing relevant scores and plotting of plots 28693, The distribution of the target variable is positively skewed meaning that the mode is always less than the mean and median 42298, Here I have followed the ensemble method to train the model and used the Keras Sequential API where you have just to add one layer at a time starting from the input 43046, Data exploration 11639, Extra Trees 21906, Dropping outliers in the highly correlated variables 18967, Display the density of two continuous variable with facet of many categories 16822, Feature Scaling 26705, Plotting Sales Ratio across the 3 categories 19413, As stated in the evaluation page 19515, Creating an Empty RDD 3594, Check more accuracy alpha 18244, merge both train and prop 1375, To complete this part I now work on Names 25881, Plotting of meta features vs each target class 0 or 1 3351, Bining Deck feature 31772, Accuracy 11422, Find 693, that the convergence is confirmed let s calculate the confusion matrix of our model 20199, Removing Multicolinearity using VIF 20550, Predictions and Submission 32906, the 17887, Continous Variables 39673, Specify our network structure which take in X and pass it through a number of layers 13889, Passenger s Tickets 32853, New dataset 34642, SVC Classifier 8869, Skewness Check in the Column to be Predicted 20058, Create a model 7208, Lets get to know what our dataset looks like 7571, Drop columns with lots of missing data 24484, Evaluate the model 22773, Declare Hyperparameters 36539, The target as well as the ID Code of a sample are 2 special variables 20272, Convert categorical string values to numeric values 36865, Random Forest Classifier 9211, Survivors by Salutation 25393, For the data augmentation we choosed to 37344, Preprocessing 1361, This model uses a decision tree as a predictive model which maps features to conclusions about the target value 20884, Define early stopping callback 43361, Prediction and Evaluation 27012, Neural Networks 802, Final Tree 22776, Training the model 20632, We would also remove embedded special characters from the tweets for example earthquake should be replaced by earthquake 18720, unfreeze our model and run fastai s learning rate finder 30269, RandomForestClassifier performs clearly better 10138, Submission Files 15624, Ticket Type Feature 42863, We prepare Keras Inputs for the different features in the data 10614, Looking at dependancies of values for each code Pclass code 11283, The RFECV selector returned only four columns 10798, Again categories below 45 items are not respresentative 38195, Grid search is a way to find the best hyper parameters by evaluating all possible combinations of these 603, we continue to examine these initial indications in more detail 42964, Name Feature 16985, Neural Networks using sklearn 4103, Split data train test 12722, Model re training 7003, Low quality finished square feet 6707, Checking missing values by below methods 26079, Function to drawing learning curve history learning neural network 13749, Convert Features 16514, Gradient Boosting Classifier 17454, Deep Learning in progress 42049, Replace spaces with underbar 10083, We shall preprocesses total data based on training data not on the basis of total data as that would make the data biased towards test set 12416, To drwa heatmap for correlation 6587, K Fold cross validation is common type of cross validation is performed by partitioning the original training data set in to k equal subset 36852, plot confusion matrix 18538, How are numeric features related to SalePrice 817, Plots of relation to target for all numerical features 15820, we don t need Passenger id Name Ticket Cabin Age bin Fare bin 1393, Explore the Data 1061, I tried numberers that round alpha 0 14730, we can split our training and testing data and fit our model 16549, make sure it s done 4232, Feature reduction 11654, Artificial Neural Network 237, Model and Accuracy 25391, A loss function to measure how good the network is 7001, Total square feet of basement area 32599, The final plot is just a bar chart of the boosting type 8498, LIME Locally Interpretable Model Agnostic Explanations 36428, Consolidation of Data 18747, Selecting 39153, Evaluation 9409, Cleaning encoding categoricals and simple imputation 8265, Checking for any Extra Missing Values 15496, Output Final Predictions 19135, Model 1 input 784 sigmoid 512 sigmoid 128 softmax output 10 9263, Looks linear but two outliers very evident 447, Since area related features are very important to determine house prices we add one more feature which is the total area of basement first and second floor areas of each house 18981, Display more than one plot and arrange by row and columns with common x axis 22264, Gender Age Group Probability by Model Benchmark with CV 14655, Dropping Name 5449, Make a function for all features 42073, Each of the models are fitted on the Train set and evaluated using Test 4720, splitting the training data into training and validation set 1996, With 81 features how could we possibly tell which feature is most related to house prices thing we have a correlation matrix 8602, Handling the null values in both datasets 27470, Word Clouds 20966, Adding Input Layer and Hidden Layers 925, Optimize Ridge 13734, Confusion matrix and accuracy score 3706, KNN Regression 7135, Sex 6741, TotRmsAbvGrd Vs SalePrice 22071, Starting the training 27468, Tokenization 23396, pick an image and cycle through the layers making a prediction and visualising the features outputted by the layer 31570, ToTensor 11251, gradientboostingregressor xgbregressor randomforestregressor adaboostregressor ridgecv stackngcvregressor 35534, concatenate both the training and test datasets into a single dataframe for ease of data cleaning and feature engineering 37107, Code in python 33695, I utilize HOBBIES 1 234 CA 3 validation item for few basic plots 10446, The numerical variables having correlation with SalePrice are dropped 9864, Pclass vs Survived 21143, Having some information about the target we choose 3 interesting numeric variables to analyse whether they are correlated in any way with our target 27031, If we test whether a sample is positive we can get another hypothetical test 32065, Principal Component Analysis PCA 30419, Define tokenizer 24862, Generating the submission file 14708, LOGISTIC REGRESSION 41577, Transfer learning refers to using a pretrained model on some other task for your own task 41130, Prophet Forecasting 38748, we can calculate the correlation 21998, Using PCA 35236, Text font 35820, Train model 33460, DayOfWeek Open 24545, Again we exclude the dominant product 31119, Feature selection by xgb 3779, RandomForest 27918, XGBoost 4218, Data Transformation 39120, Perceptron 2377, Vectorize two text columns using ColumnTransformer 14914, Fare 6215, MiscFeature Miscellaneous feature not covered in other categories 13074, Submission 37075, Training Model 14418, Check for NULLs Duplicates 7799, Blend Models 41021, Make WCG predictions and submission to Kaggle 12566, we got 2 NaN values left so we simply remove the two entries 31059, Positive look ahead succeed if passed non consuming expression does match against the forthcoming input 6495, linear models perform better than the others 8376, Feature Engineering 13524, Predictions 7808, Tune Models 33746, We can now fit the parameters 29977, Util class 14841, Fare 42867, Run random search over the search space 1643, Explore the size of the data 37015, Top 10 categories by number of product 34255, Test RMSE 4205, Inconsistent types 11543, Before starting our analysis it is important to separate our data intro training and testing set 16936, The second stupid model is rich woman 41597, Build the model 14736, True Negative Rate 24808, there is no such columns like people s name house s name good 13975, Create a new feature Family size from the features SibSp and Parch 30890, Find out the most completed column which could be use as a reference to impute the missing value 15081, Logistic Regression 14812, Parch Survived 36887, dense 2 43252, Saving prediction trained with SVM 41410, DAYS EMPLOYED 14992, To check missing values 35423, plotting the training and validation accuracy plots 26542, Create Model 24395, Learning schedule 10987, Again let s check the correlation in our new features 42732, There are 60 combinations which have larger correlation values than 0 8849, Instead of separating the data into a validation set we ll use KFolds cross validation 6188, Explanation of Violin Plot 31522, The optimal parameters can be extracted through the model 7859, The Finale Building the Output for Submission 24705, Optimise on metric 22382, Imputting missing values 36132, In this kernel we implement EfficientNetB7 a framework that was released by Google AI in 2019 which achieves much better accuracy and efficiency as compared to the previous Convolutional Neural Networks 20249, Drop the colums that are no longer needed 7492, Check out the survival rate for each port 8276, Box Cox Transformation 5611, Merge By CHAID 36917, How many Survived 20950, Setting network parameters 25780, Survival probility as per familysize 14161, In addition we can also derive the marital status of each member from the Name feature 28816, EDA 12188, The validation set is 20 of the full training set and it correctly reproduces the proportion of houses in each Neighborhood 20096, Modeling with Prophet 41600, Evaluate accuracy 7435, Randomized Search 17471, DecisionTree 19386, Split dataset 10342, The only missing values that are left are within SalePrice which is exactly the number of lignes in the test data 34972, Final Model 18357, One benifit of using a tree based model that it can provide us with the best features it uses to split on In this final section i am going to check if doing a fecture selection of the top 1 3 features can help improve model any further 29694, Final Checking 29475, Featurization through weighted tf idf based word vectors 2303, Categorical vs Continuous 7283, Modelling 42644, Some other basic NLP techniques 14675, Submission Predictions 10084, If there is no garage there is a null value for all garage fields so let s put NA to all such fields there is an outlier with which we convert to because if there is no garage there should be no Garage Area which means it may be an artificial error error in data collection etc 19278, That was a surprisingly large amount of 2 s 11729, Quadratic Discriminant Analysis 19554, Model Evaluation 5371, Filter Methods 36381, Visualizing Images 20114, Total sales trend 5673, Create a new Fare Category category variable from Fare feature 23483, Custom Preprocesser 493, we ll fill in the missing values in the Age feature 20445, POS CASH balance 41613, let s only take columns that we need for this analysis and put that data into a new table called reorder merged 30915, Embedding 958, I have not yet figured out how to assign and store the feature importances outright 22027, Correlations 5290, Feature Engineering 24560, Distribution of products by segment 22140, 1stFlrSF First Floor square feet 21160, The scores and rank should match the public LB scores as of 31st May 2020 midnight 42527, filled all the nan value with a string nan this string could be anything 32312, Relation between Survival and Port of Embarkation 9627, Ridge 5667, First Name 12605, First check the impact of feature Sex on Survived 29027, Concatenate train test 2524, Voting Classifier 196, XGBoost 17553, First class people have more count of survival 32600, The Ba optimization spent many more iterations using the dart boosting type than would be expected from a uniform distribution 23265, Outlier Detection 28861, We wil use a sliding window of 90 days 23477, Since we know that the output should never be less than replace all negative values with 8815, Filling missing values in Age 21905, The last two values are out of trend and are outliers 28622, MasVnrArea 12201, The normal OneHotEncoding or the standard get dummies would create a dataset with only 3 columns and when a model is called an error caused by the mismatch in shape 11060, Pclass versus survival 41113, The mean median and mode of a normal distribution are equal 19667, Feature Importances 37896, Feature Importance 28411, Generating submission File 34961, Pclass was not included in the passenger df as input to the K Means clustering model 10873, we split back comb2 as we are done pre processing 10039, Extract feature in Cabin variable 18702, pass in the new learner to DatasetFormatter 9773, But because completition s data provider does not give us a correct answer for the prediction 25797, This is more clear if one looks at the plots for the cumulative distributions 27953, Use the next code cell to label encode the data in X train and X valid 16823, Drop Already existing Columns 5440, i 3 Split at 3rd element 20858, All 28100, Prediction based on test data 40716, Visualizing the digits by plotting Images 27599, Dual LSTM Layers 25241, Box Cox Transform 29348, The plot confirms 1st class more likely survivied than other classes whereas 35911, Prepare the model 17813, plot the features importance 24879, fill the Age of missing people with missing Age 32757, Save All Newly Calculated Features 15798, Grouping Fare and creating a new column called FareGroup with their means by Pclass 1745, Embarked is a category variable 18, GridSearchCV Ridge 8493, The library to be used for plotting PDPs is called python partial dependence plot toolbox or simply PDPbox 14546, Survival font 20200, Deal With Outliers 30959, Hyperparameter Tuning Implementation 22210, Creating Submission 16609, Separate dataset into train and test 37288, Our threshold was set to 6579, This didstribution is leftside skew to resolve this there are two possible approches 27055, Make validation predictions and calculate mean absolute error 15298, Perceptrons Model 24231, Prepare generators for training and validation sets 28277, Combining the synthesized data with the actual training data 32693, have a look at the correlation matrix 9250, Encoding the data 1809, Imputing Missing Values 38420, You need to scale a data before train the model 10999, Lets hear what categorical variables tell us 11646, Suport Vector Machine 152, Voting Classifier 16867, Plot tree 4135, Visualizing Feature Correlations 2647, We can fill all the NaN values using fillna 18537, Check for missings 9641, we shall se that how they affect the Sale Price through easy yet informative visualisations 43272, Importando a fun o mean squared error para avaliar o desempenho do nosso modelo 22819, Once again using Google translate I found that this is an antivirus sold to 522 people and the price is probabaly the cost of one installation times 522 7326, Replace the missing value of Cabin with U0 34300, The 28th channel appears to focus on the white coloring around the mole on the right side 4505, Multivariate analysis 31747, RandomVerticalFlip 13202, First let s split titanic dataset into train and test again 9484, Class Prediction Error 33672, Weekday font 9202, Family Name Extraction 20972, Make Predictions for test data 4907, Generate the Correlation Matrix 4899, print our optimal hyperparameters set 23693, Create databunch 10237, Above Models are giving good score but this can be improved let s try K Fold techniques to check model accuracy 1245, visualize some of the features in the dataset 26246, Sanity Check on 9 Images 38518, Exploring the selected text column 12719, Round 2 Feature selection 8584, Save the predictions 10092, We use lasso or elastic net in final predictions 41678, lets take a look at some of the resized images of Malignant Tumours 3950, Changing Types 41334, Make final predictions using our decision tree classifier with t SNE features and make a submission for Kaggle to measure our own validation against the public leaderboard 15788, we had earlier made a X test for us lets predict values with that 37345, Add first layer 32580, Optimization Algorithm 29630, Logistic Regression 5357, Diplay relationship between 3 variables in bubble and two more variables affecting size and color 7825, GradientBoost 29923, Boosting Type 24037, Forming splits for catboost 24264, Observations here are the survivors 36135, Preparing the data 34029, TEST SET 9467, XGBoost 22506, Converting Dataframe into ndarray 23673, We want to train the autoencoder with as many images as possible 39731, We have two labels for Sex with females having a much higher survival rate 3994, you can use our trained model for forecasts 24747, now make this data clearer by plotting it in a graph enter barplot 26947, Removing that data point 4296, sklearn 26943, If the magic number is removed the distribution 33707, Color Gradient Gantt Chart 28352, Checking the Correlation Between The Features for Application Train Dataset 35089, Error Analysis Training Set 2656, drop columns which are not useful to us as of now 5001, Univariate Analysis 16278, Add some regularization 34449, Items Sold 29166, KitchenQual Fill with most frequent class 27167, Before that we need to convert few features datatypes back to int from object that we had change in the start 1891, We can also use a GridSearch cross validation to find the optimal parameters for the model we choose to work with and use to predict on our testing set 23533, Discussion and some more examples 9167, I feel like I could merge 3 and 4 into a single category as Above 2 2403, Loading and Inspecting Data 14001, Children and women go first 13799, pandas profiling library 12428, without regex 13582, Dummies 32188, among RMSprop Adam Adagrad SGD adadelta the best optimizer for this particular model was Adadelta 5149, Numerical variables 40023, Probably the most interesting information is given by Rows Columns and Pixel Data 18375, Train Test Split 35526, In this part some hyperparameters were tunned and find the optimum ones for model building 42802, Exploration of target variable 28471, When do people usually buy houses 41483, Data Wrangling Summary 37483, Logistic Regression 17390, Pearson Correlation Heatmap 7471, First checking how the data looks like in both files 37618, Categorical Variables 268, Prediction and Accuracy 36765, Building a Convolutional Neural Network using Keras 31399, Evaluate the model 16085, Pclass vs Survival 1697, Detecting missing data visually using Missingno library 21265, In this section we are trying to find the learning rate setting up a pretrained model 27640, Numpy and pandas help with loading in and working with the data 26104, PCA 5152, Feature Scaling 8573, Applying a Function Over All Elements in a Column 3815, Clearly this is not a normal distribution as the titanic contained far fewer 10 20 year olds than 20 30 year olds 19328, Model Building Process 21319, Explore file properties 2016 and 2017 28160, Training loop 16493, Features Scaling 34752, Loading embeddings 22236, Feature Enineering 12888, we can plot this since we have extracted a categorical feature from a qualitative one 4469, Dealing with Cabin Missing values 15167, Neural Network 3391, Variable Variable name 34275, I want to check what is the accuracy in predicting the right interest level 41331, that the t SNE features are created we train a new with the same hyperparameters 40799, Mean and median outcome group by group 1 2185, Learning curve 12275, Dealing with the NA values in the variables some of them equal to 0 and some equal to median based on the txt descriptions 27207, Submission Part 23582, Testing our tuned model on the Test dataset 7141, Pclass 6768, XGBoost 35086, As the best Hyper parameter turns out to be C 29547, Punctuation 30522, Target Variable with respect to Walls Material Fondkappremont House Type 32323, Create final file for submission 42807, best score 0 42654, We determine the indices of the most important features 30255, Have a look at the most important predictors 131, AUC ROC Curve 11415, Use Case 7 Pricing of Books 17455, Test 28887, We ll be using the popular data manipulation framework pandas 16632, Missing values 15027, Young passengers get a higher Survival ratio and old passengers get a lower Survival ratio 41867, BIGRAMS 3458, we construct a random forest classifier object with them 24243, Feature Engineering Bi variate statistical analysis 9099, This could be a useful feature so let s keep it 19432, Check the number of unique values in each column 33499, Albania 30953, Take each model s probability mean as final ensemble prediction 21631, Explore a dataset with profiling 18318, according to google translate top values are 31431, find original Jaccard scores for the same data 11175, We choose number of eigenvalues to calculate based on previous chart 20 looks like a good number the chart starts to roll off around 15 and almost hits max a 20 23733, Feature Engineering 35058, I compiled this model using rmsprop optimization categorical crossentropy for loss measurement and accuracy as metrics measurement 4076, Relationship with numerical variables 23457, Season 27176, Gradient Boosting Regressor 41479, Final Data Preparation for Machine Learning 18525, In order to avoid overfitting problem we need to expand artificially our handwritten digit dataset 8288, Stacking 35490, Visualizing training Curve 18755, Each CT Scan consists of multiple 2D slices which are provided in a DICOM format 29796, Building Skipgram WordVectors using gensim 28808, UCM 6278, Family Size 6354, Log transform skewed numeric features 40486, Extremely Randomized Trees 33793, The distribution looks to be much more in line with what we would expect and we also have created a new column to tell the model that these values were originally anomalous 646, investigate the Fare affair in more detail 35469, Visualiza the skin cancer lichenoid keratosis 22854, Add month feature 21802, Model 3 Transform skewed data 27664, Prediciting the Outputs 4541, scikit learn 6772, load train test dataset using Pandas 20583, Dropping unnecessary columns 23430, Data Cleaning 15586, This number looks more like something I could believe 36736, Random Forest Model 17857, Create the Out of Fold Predictions 5157, scaling dataset 17693, CONVERTING CATEGORICAL VALUES INTO NUMERICAL VALUES 17650, Gradient Boosting Decision Tree 24167, Build Model 40087, General Feature Preprocessing 15833, Split train and test 15864, Submission 13451, approximately similar to 79 24530, Total number of products by age 17962, pandas 6920, SUBMISSION 3874, How hot is a recently remodelled house 20608, Ticket 33873, Random Forest Regressor 42407, Visualize sample rows of the submission predictions 42888, Laboratory 37317, Data preprocessing 35470, Visualiza the skin cancer solar lentigo 26416, The titanic had three different stops before it went on to cross the Atlantic Southhampton Cherbourg and Queenstown 1233, Pairplot for the most intresting parameters 18373, Create Dummy Variables 15239, Using sns 11353, Check relationship between numerical variables and Sale Price 20251, Machine Learning 16500, Gaussian Naive Bayes 29138, Correlation plots 43313, We can bin the Age and Fare 12166, Min sample leaf 31428, And apply the formula 21911, Missing Data 22635, Test Function for Modeling calculate RMSE for various tests 5970, I train the data with the following models 8257, Creating a Visualization of every feature with missing values 19172, Standardize 19533, Perfoming Joins on RDD 11371, Import libraries 33352, Timeseries autocorrelation and partial autocorrelation plots weekly sales 15058, PS The old man with nan fare what s his ending 26890, Create Submission File for approach 5 24027, Since the ammount of opened and closed shops is different all the time to separate seasonal effects I adjust item sales by the ammount and size of shops that are opened at any given point 22536, Confusion Matrix 37803, Random Sample Consensus RANSAC 11970, Categorical Features 32993, PCA 15491, Grid search 31938, set up a validation set to check the performance of the CNN 96, Facetgrid is a great way to visualize multiple variables and their relationships at once 8340, it is time to have fun with the stacked emsembling model 10769, Formatting for submission 7455, Missing values in Embarked column 34247, Normlize Data 7878, Highest fare mean improve the chance of survival 27446, This is very interesting notice how 1 2 and 3 are repeated twice in the platform once as floats and once as strings 13543, Summary of df test 28059, Similarly the first class passengers are more likely to survive than the second class 17885, Lets look at the data 38251, Submission 37354, Cabin vs Survived 17272, K Nearest Neighbour 42150, Encoder 29139, Correlation of integer features 10097, devide data into X Y 19099, And that s the basics of Support Vector Machines 11446, Replace missing data for Embarked 7706, we fit the models on the test data and then submit it to the competition 4375, Something Interesting 31072, DATA CLEANING 28256, We shall look at distribution of target variable later 7036, The building class 22607, Producing lags brings a lot of nulls 3211, At last we have a lot of features regarding the outside porch in all its types 8239, Visualizing a Tree 23670, Holt linear 28228, Below we generate an image that contains only pixels plucked from a uniform distribution between and compare it with the autoencoder generate image 23458, Weather 17692, SURVIVAL vs INITIALS 42468, Creating interaction variables 28247, Visuliazation 20494, Train Image Embeddings 18750, Grouping and Aggregating 42397, How do SNAP days affect sales 14447, go to top of section engr2 40826, Some of the inferences that can be made 38935, By mean of both Random forest and Xgboost 35946, ADABoost 11456, The Cabin feature needs further investigation but it looks like that we might want to drop it from the dataset since 77 of it are missing 24537, Number of products by customer s birth country in relation to the bank country 3455, Here s a correlation heatmap for the predictors in the Deck training set 15346, SUBMISSION FILE choosing Gradient Boosting 12638, Using the Mean for Age 16705, replace by ordinal values 32417, Parameters weights of the Convolutional Layer 8663, Instructions 3789, SalePrice 43295, Testando sem a Coluna Day 16615, Feature Fare 42951, SibSp Feature 36414, BoxPlot Continous To find Outliers 43033, Normalizing the Data 2280, Conclusion 31191, Using Cross validation 31319, Applying Log Transformation of the Target Variable 35602, Running 20581, Checking for missing values 39816, Clearly 255 is the range so we now use this value to scale our data 12245, Array math 35584, ML Model 37535, Importing Pytorch Libraries 33605, Check the frequency of each digit in our training data and how they are distributed 31730, Patients 31647, MODEL 3 Conv1D 37076, Cross validation Evaluating estimator performance 1424, Decision Tree Classifier 9696, check for the missing values first 21814, Longitude and Latitude 23978, switching to 224x224 size which is usually used for ResNet 50 28299, Apply model to validation again 14603, Random Forst Classifier 170, fit the model to our train set We use Scikit learn which is our favourite library when it comes to Machine Learning since it includes every standard regression classification and clustering algorithm Deep Learning is a different animal if you are interested in this area stay tuned 20821, As a structured data problem we necessarily have to go through all the cleaning and feature engineering even though we re using a neural network 34245, Plot The TS 6573, Sex 40041, Creating a hold out dataset 27746, Submission 36731, Save submission file 25648, Imputation 3803, Lasso 36122, our numerical columns are set now its time to encode the categorical features 34516, Previous Applications 15978, Cabin Feature 35833, For handling the numercial missing values we use the features mean 21726, Avalia o dos modelos 12366, Exterior2nd Exterior covering on house if more than one material 10964, Basement garage fireplaces and other features 31566, take a look at the dataset 3828, concate both train and test data 26053, The evaluation loop is similar to the training loop 15741, we have to focus categorical features because for ML algorithms we can not use it Pclass Sex Embarked Title 8327, Essentially object is string There needs to be a way to convert string to float or int This is where LabelEncoder kicks in 43406, There are several commented out maximize lines that could be worth exploring The exact combination of parameters determines exploitation vs exploration It is tough to know which would work better without actually trying though in my hands exploitation with expected improvement usually works the best That s what the XGB BO maximize line below is specifying 20215, Reshaping 34406, Preprocessing 18992, Optional Dump assets to disk 18307, first item appearance feature 42041, Insert new column at a specific location 19436, Split into train test csv with highly correlated variables float removed 2123, These are 32768 different configurations to test on 5 folds 39267, 1st order features SPATIAL DISTRIBUTION 1349, Converting categorical feature to numeric 22594, Monthly sales 36524, Train Test Split 18635, From 2011 the number of customers becoming the first folder of a contract in the second six months is much higher than the first six months in a calendar year and it is across all years after that Looks interesting to me from a business standpoint 30377, Model Evaluation 21848, Understanding the Model 16910, fillna Fare 24787, Text Data 12506, The best eta value in this case looks like it s Add it to the parameters Since this is the last parameter we re going to tune we can also pay attention to the number of rounds the model took If we don t want to use early stopping we can set the number of rounds manually to essentially num boosting rounds is the final parameter to tune it changes so much based on the other parameters that it s key to tune last 11667, Feature selection 31939, Run the model using the train and validation datasets and capture histories to visualise the performance 39415, Where do we have NaN values 13328, let s store in a new binary column the rows where the Age value is missing 8418, Neighborhood 32071, Projections Based 8481, Gradient Boosting Regressor 2996, Fixing Skewness 6389, To check whether female passengers were more likely to survive we conduct 2099, Or of ExterQual 16238, The third classification method is Linear Support Vector Classifier 7297, Univariate Analysis 7288, KNN 14134, As the fare range increases the changes of survival increases 13082, Looking at out best features 20690, MLP for Regression 10159, Bar Plots 11336, Outlier Treatment 27982, skewness and kurtosis 15131, Name 16973, Great We dropped all the outliers from the train dataset now we have to localize those outliers and drop them from the target survived to keep the same shape of the train and target sets 8040, Similarly For testdata we perform same action 1356, We can use Logistic Regression to validate our assumptions and decisions for feature creating and completing goals 20661, FILLING THE DATA WHICH IS LEFT NOT FILLED 41213, start Frist we log the train KernelNB as input data 36519, Ticket 8927, FireplaceQu 7805, Stack Models 19067, Load the sample submisson 6336, Before cleaning the data we zoom at the features with missing values those missing values won t be treated equally 4595, SUMMARY For garage features we will 21587, Keep track of where your data is coming when you are using multiple sources 42629, Line plot with Date and Fatalities 6837, Dealing with the Skewness 3628, Features Porch Bathroom Square Footage 14261, Predictive Modeling 30978, we must slightly modify random search and grid search to write to this file every time 16587, Lets Check first from where 1st Class passesnger Came 6210, Naive Ba 1309, Observations 30914, Tokenize 25411, CROSS VALIDATION 11363, Functional data description says NA means typical 42613, begin by reading in the training data and taking a look at some sample images 33652, Split features and targets from the data 9429, Color the points using a second categorical variable Sex 13430, Determining the missing data 25649, Run the next code cell without changes to obtain the MAE for this approach 28297, look at best saved models 43265, Treina a rvore de decis o com base nos dados de treinamento e diz quais s o as respostas 4170, BoxCox transformation 11682, you ll make predictions on your test set create a new column Survived and store your predictions in it 30633, Relationship between sex and survival rate 2448, Price Segments 17645, k Nearest Neighbors 12271, Linear Regression 32656, Visualization of distribution and correlation with the dependent variable BEFORE normalization p 41360, Fireplace quality and sale price are correlated 41752, Predicted value is low we can give credit here 25377, In order to increase the training data the original digits are transformed to reproduce the variations occuring when someone is writing a digit 8688, INFERENCES 31299, Train Our Model With Cats Dogs Train splitted Data Set 28964, Continuous Variables 2425, Linear Regression Model 844, Elastic Net 26818, check the distribution of the mean of values per columns in the train and test datasets 29338, do one last ensemble using XG boost 33589, Test Time Augumentation 35817, Extra interaction features 38482, Convert data to a tensorflow dataset font 41370, All houses have all the utilities so this feature is useless for us 5514, SVM Classifier 18584, First look at cat pictures 1948, SalePrice Correlation 27264, Calculate priori probability P and P 4033, replace the skewed SalePrice column with the normalized log10 data which be used for the regression prediction model 7257, Data Analysis 108, title 11087, Missing Data 38701, Mean Color 6745, Box Plot 22227, Tam ba lant Fully connected 3501, As with the random forest model a gradient boosted classifier can provide us with information about variable importance 9118, Continuous Variables Distribution 40715, Frequency plot for the validation set 13395, There are 4 variables that need to be categorical encoded 34610, Prediction using XGB 15699, Embarkation point vs Survived 11436, Categorical Variables Get dummies 33709, FEATURE Passenger 38724, that we have changed the generator and discriminator let s compile them into a single model 17019, Cabin 22420, for the main event 20463, Education type of the client 41059, Training 40263, Full Bathrooms 31315, Fill Promo2SinceWeek with 0 8517, Test Codes 7657, For the other model parameters I referred to this 7850, First things first A Random Tree Regressor 1211, Quiring the data 28704, The new training dataset is 438 rows of predictions from the 8 algorithms we decided to use 8352, Class and age distribution 36821, We need to remove punctuations lowercase remove stop words and stem words All these steps can be directly performed by CountVectorizer if we pass the right parameter values We can do this as follows 26285, Initialize the model s parameters 10666, K means Clustering 18094, The metric that is used for this competition is Quadratic Weighted Kappa 11901, Vlidation 35141, Initial CNN model 23, RandomizedSearchCV SVR 35792, Plot prediction vs actual for train for one last verification of the model 20111, Item count mean by month city item for 1 2 3 6 12 lag 4706, 3 of these models have selected features 32097, How to generate custom sequences in numpy without hardcoding 32505, Pre processing the features 1665, Good no missing values for Age now 2507, Fare Range 11433, Intersection of biOut and uniOut 16240, The fifth classification method is RandomForestClassifier 9583, Printing emojis with Python 25793, use One Hot Encoding for Categorical data 5427, Basement 28795, Modelling the Problem as NER 21382, In order to make the optimizer converge faster and closest to the global minimum of the loss function 30407, Miss labeled data 30414, Train and predict 17979, Our dataset is almost ready 27163, We have four year features in our dataset 21653, Merging datasets and check uniqueness 2405, Since there are so many columns to work with let s inspect the correlations to get a better idea of which columns correlate the most with the Sale Price of the house 19394, Organize data for visualization 16877, Embarked apparently affects survival rate but is it really 6417, SalePrice looks good now lets handle other numeric variables 30874, Predictions on test set 25860, Linear Model SGD Classifier 30252, Preset xgb parameters 33142, Don t forget that the latest model might not be the best Instead the best is the one we saved as model 3792, Scatter Plot 13466, Exploration of Fare 17763, Load packages 19176, LGBM 12267, EDA 25269, Model Architecture Design 9762, Dataset Cleanup and Spliting 10632, SVC 15575, Feature preparation 25298, It is not finding the tips but the points with the greatest distance from center look at leaf19 11813, after gaining a more in depth view of Sale Price we can conclude that 9733, I define an SVR classifier with parameters that have been tuned separately 6393, NULL VALUES 36789, Normalizing Text 11350, Output Submission 18432, Gradient Boosted Trees 13377, Remove redundant features 7873, introducing new features as Family size 25880, Creating Meta Features 25199, Data Augmentation 21932, Save cleaned datas 29595, up is the learning rate finder 35473, Visualiza the skin cancer unknown 38290, Dealing with nulls in LotFrontage 10696, Embarked processing 9821, Each passenger Name value contains the title of the passenger which we can extract and discover 40696, NOW LET S TUNE A BIT 30621, According to Titanic Facts p 41460, Plot survival rate by Sex and Pclass 9755, FamilySize 27925, Observe the reduction of RMSE value and the iterations stops when there was no longer an improvement 13936, This feature is from this kernel 42556, We use the best estimator found by cross validation and retrain it using the best hyper parameters on the whole training set 29961, Combinations of TTA 42093, Importing necessary libraries 20393, Support Vector Machine Model 7645, GarageCars and GarageArea are highly correlated variables which makes sense since the number of cars that fit into the garage is proportional to the garage area 8299, AdaBoost Adaptive Boosting 20461, Ocupation of client 36138, check out the scores of KNeighborsClassifier and RandomForestClassifier without hyperparameter tuning 28340, Analysis based on FLAG OWN CAR 23044, we can do the same thing in item category 11078, Second Level Dataframe 32253, Library 5397, Here I look deeper to make sure there might be some special guests with special title 24340, Aside from normal stacking I also add the get oof method because later I ll combine features generated from stacking and original features 1638, I m using ElasticNetCV estimator to choose best alpha and l1 ratio for my Elastic Net model 3916, Transform Categorical to numerical 11876, Dummies 940, Convert Formats 8343, Alright so far we have three models 33478, And finally to obtain the evolution of the disease we simply define the initial conditions and call the rk4 method 12389, Here in order to make the machine learning model I have taken the threshold to be 0 37815, Do people go for lengthy tweets in case of disaster 13051, we have the training and test datasets available and we can start training the model 5915, Testing on test data 17673, People who have their rooms on the right of the titanic have more chance to survive 16214, Importing Libraries 12272, Comparison of the all feature importance diagrams 27386, testing the impact 18333, Neighborhood 14444, go to top of section engr2 17006, Missing values Fare and Embarked 29165, Functional Fill with Typ 5280, Permutable Feature Importance 1275, so there are some misspelled Initials like Mlle or Mme that stand for Miss 13149, explore a IsAlone column created from FamilySize 12765, it is time to design the ML Pipeline 7475, 3a Sex 35238, We would know the proportion between text and selected text 22384, Prepraring data for prediction 1773, Dropping Useless Features 30469, Tune the parameters of a VotingClassifier 6867, Dealing with 2nd floor 213, Model and Accuracy 40470, One Hot Encoding 27061, It s clear that the rest of the texts are hided because of the test length I put the column in a list to check the first 5 texts 21188, How does logerror and abs vary with time 4298, Submit Predictions 6971, Random Forest 36652, Normalizing image arrays between 0 and 1 is very beneficial when training deep learning models because it helps the models converge and train faster 34045, Engineer a feature to indicate whether the crime was commited by day or by night 5364, Diplay charts with slider option 34730, Importing necessary modules 13032, Additional analysis 5174, Support Vector Machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis Given a set of training samples each marked as belonging to one or the other of two categories an SVM training algorithm builds a model that assigns new test samples to one category or the other making it a non probabilistic binary linear classifier Reference Wikipedia 4740, Missing value of each rows percentage 36372, Fit the Model 3791, SalePrice Distribution 18290, I also apply padding mostly to store X as an array 19949, There is 17 titles in the dataset most of them are very rare and we can group them in 4 categories 19599, Number of days in a month 25044, Department Distribution 3528, Box plot OverallQual 14905, Embarked Pclass and Fare vs Survived 31623, dumb classifier 38623, Creating Callbacks 15603, Create simple model 15306, Training Random Forest Model Again 2345, XGBoost eXtreme Gradient Boosting 36944, Target Encoding 23179, Model Selection 41999, Sorting columns multi columns in ascending order 17700, RANDOM FOREST 40311, Main part load train pred and blend 24495, Basic train test split at 20 test data 80 training data 7383, I merge two datasets based on WikiId 35178, Conclusion this case in similar to batch normalization The general behavior looks better when MaxPooling is not replaced by convolutional layers but the long term tendency is better for convolutions with strides Given that our objective is to increase the final CNN accuracy I decided to keep this replacement which I verified to be better through submissions 965, There have been quite a few articles and Kaggle competition winner stories about the merits of having trained models that are more uncorrelated with one another producing better scores 10749, Combine train features and test features and create dummy variables for character features before runnning machine learning models 22421, I ll build a function that convert differences month to month into a meaningful label 8743, Label Encoding 34819, Label Encoding 23213, We ve made our submissions using different ensembles now compare their submission scores with our best base models submission scores 9594, Statistic charasteristic of only one feature Age 38667, K Nearest Neighbours 37292, Basic EDA 39094, The Flesch Reading Ease formula 33300, Correlation Matrix 32979, Features standarization 24056, Feature Generation 11968, we can remove PoolQC MiscFeature Alley Fence as these features contain more than 80 of null values 16917, Final data 7092, TPP 1 means highest probability to survive and TPP 3 means the lowest 24464, Lets begin remembering how GANs work 16627, Build Final Model Using Best Threshold 19520, Using Range to Create RDD 24943, Q Q plot after taking the logarithm 12882, It looks like most passengers were alone and passengers who were alone typically had lower survival rates 11708, Check the work 9693, Converting some numeric variables to categorical 39186, N mero de crimes por lacalidade 13226, GridSearchCV for XGBoost 24479, Training for 30 epochs this strategy achieves a validation score of 0 22755, We achieve this by setting gamma 23375, Visualise images 1065, ENSEMBLE METHODS 33026, Training the model 28328, Exmaine the installments payments dataset 29179, Look how much features are correlated by using the Correlation Plot 29456, submit your prediction of this model with 42783, Choose model architecture and compile 5094, Once the data is cleaned we can proceed to the cake in our party e 4971, Age and Fare bins 12257, Besides MSE when we only have one feature we can visually inspect the model 15088, K Nearest Neighbor 10585, Evaluating ROC metric 12157, Split the data to train and test sets 40078, Linear Regression 14520, Observations 10897, Label Encoding 1273, Feature Age 3 2 3 23266, Pclass 6946, check the quality of clustering with dendrogram and silhouette plot 38298, There are no null values present either 20948, One Hot Encoding 25454, Preparing data and generating their labels 43058, The next 100 values are displayed in the following cell Press Output font to display the plots 9381, check any missing value present in TotalBsmtSF feature 19174, PCA unused 37212, Functions Compare Training and Embedding Vocabulary 34789, Clearly weekend and weekdays follows a different pattern 12526, now we add the categorial missing values manually 12372, Verifying if all null values are removed 1849, Collinearity 37642, Log of Price as it is right skewed 30567, for the new variable we just made the number of previous loans at other institutions 30963, The boosting type and is unbalance domains are pretty simple because these are categorical variables 5022, Partition 34924, Text preprocessing with NLTK 29038, Testing set accuracy 6776, SibSp of siblings and spouse 19884, Date Related Features 6000, Kita buat function untuk melakukan impute sekaligus membuang fitur penyebab Multicollinearity 24434, Feature matrix and target 34422, we do a bigram analysis over the tweets 14268, K Nearest Neighbours score as we change the n neighbours attribute 8241, Correlation Matrix 20742, GarageYrBlt column 57, DataSet Library Loading 4791, Base on the definition of these features give in the data description we impute the rest of the values to NA or 0 which signifies absence of that feature 32226, Great let s take another peek at our data 42566, define the neural network 33037, Random Forest Modeling 11791, Embarked Title 3022, We use the scipy function boxcox1p which computes the Box Cox transformation 9645, Seems fine 34097, look into the spread of this COVID 19 disease over the period of time 31028, Year font 42806, Gradient boosted tree was tuned with randomized cross validation 13686, I have used method to get the StringMethods object over the Series and to split the strings around the separators 37548, Optimizer 4691, Non ordered 25174, Removing Uncleaned Questions 36894, from validation data 1009, it is the size of each family aboard 10305, We re up to 87 of the variance now but the adjusted R squared is not meaningful 4515, Categorical Values 30642, One hot encoding 7490, We want to use both SibSp and ParCh into one variable 34631, Now let s find the outliers 21144, In this case let s use most common method correlation amtrix which presents the linear behaviour strength between factors 8938, Feature Importances 18358, I am going to train my model with the top 100 features derived from regression tree 22858, Training 11832, Lets find out all the categorical columns in our data 23727, Visualization 1836, Basement Finish Types 11648, Decision Tree 38891, Activation Function 23476, Predicting the output over test set 40967, A Simple Nueral Network You can try your hands on changing the architecture 2873, Preprocessing 34453, CA 2 21836, Split Training Data into target sale price and explanatory vars 8483, Bayesian Ridge Regression 30372, Label Smoothing 20789, Find the highly correlated 5450, Test it with a different data set Single Decision Tree 36223, Final predictions and submission 11426, I delete MasVnrType MasVnrARea Eletrical 28599, BldgType 31825, For ease of visualization let s create a small unbalanced sample dataset using the method 27341, Adam Optimizer it converges more efficiently 39059, that our model building is done it might be a good idea to clean up some memory before we go to the next step 13046, Most of these tickets belong to category 1 2 3 S P C 16651, Age 24897, Bruh this is seriously unexpected 38842, After 2000 number of house construction gone peak 34668, Seasonal decomposition 42213, add in the second convolutional layer same as before 35734, Since area related features are very important to determine house prices we add one more feature which is the total area of basement first and second floor areas of each house 42361, kfolds for cross validation 38510, Sentence length analysis 38508, Analyzing Text Statistics 6818, A skewness of zero or near zero indicates a symmetric distribution 146, Bagging Classifier is the ensemble method that involves manipulating the training set by resampling and running algorithms on it 18885, check that we do not have anymore missing values and procede with feature engineering 15425, let s explore the family size as a potential feature 36281, encode with OneHotEncoder technique 16460, Lets make bins for age and fare also and visualize them 24276, Decision tree 21341, Loading Library and Dataset 26348, It s time to leave 14270, Random Forests 13765, Voting Classifier 7084, We can also check the relationship between Age and Survived 22058, How is len selected text related to len text per sentiment 2539, With tf 1100, Create family size and category for family size 34841, Data Augmentation 30375, Adamax 41809, Build training model 34652, Retrieving cities from the shop names 42887, Testings 31015, We drop NA values 27595, Now we could just feed our model our word embeddings but we could also add the features we added during our initial exploration to improve performance 38678, Scaling Image Size 31281, Moving average 14955, Use only the most common titles 20662, Categorial data 15735, Create the Output File for Submission 9629, Xgboost 32841, The Model 24987, Features with low variance do a bad job of prediction 42020, Creating a new Dataframe with exisiting rows that matches multiple conditions 40847, would like to write 3 functions for different Plotly plots 13173, let s take a look into our Ticket variable 16486, Data Visualisation for Data Preprocessing 40064, Data Processing Dependant Variables Filter Outliers 10609, html 15245, Correlation 34091, Manager Id 2176, The hypothesis regarding Embarked is that it doesn t influence the chances of survival 34165, Again we keep grouping 42767, SibSp Parch 42942, Defining a function for the augmentation proceduer 41443, Below is the 10 tokens with the highest tfidf score which includes words that are a lot specific that by looking at them we could guess the categories that they belong to 21136, Ordering is finished 13680, We notice that Fare for Survived 0 is heavily skewed towards the lower end 42450, Below the number of variables per role and level are displayed 10275, Whoops It didn t really reduce any from the ones I selected 9197, Embark 2686, Forward feature selection for Regression 19175, Setting up Dataframes 6572, Create new feature using title of Name 31810, Classifier layer 21643, Create DataFrames for testing 9959, Predictions 39133, Neural Network 37362, Family vs Survived 41368, Sale Price IR3 IR2 IR1 Reg 8507, Check for Regression Assumptions 15572, Ticket group size feature 14633, First let s import our models and helper functions 17772, Go to top font 17805, Family Size is mapped then to only 3 sizes the first corresponding to the case when someone is alone 16490, I tried to fill the missing values based of my age columns for both the data set 43359, Training Test Split 17670, The function here allow to create class of age and sex and drop features not help me to predict the test dataset 18889, One hot encoding so that algorithm can understand 39072, A single line of code to set things up 35194, Yeah it is very skewed indeed 6585, we can calculate the coefficient of features to validate our decesion for feature creation 28414, Train Predict Models 28575, There are a large number of data points with this feature 0 19068, We can delete the target column as we be populating this with the probabilites 32762, We use Grayscale normalization the model work faster if the interval of data between instead of 39973, Prediction 3307, Use Ridge to find outliers 12891, To make it easier to plot I ll bin the fare based on the median values of fare at each Pclass 10876, Predicting and creating a submission file 40635, Drop location feature 12080, Overview the data 32396, Here we train our model takes a while but at the end we ll have strong model to make predictions 17574, Grid SearchCV 20163, Fitting train data and finding a score for test data to check model performance 9865, We want to make a table to understand class effect on survival For this we use groupby method 23633, that our model building is done it might be a good idea to clean up some memory before we go to the next step 29398, FIRST SET OF FEATURES 40165, We are finished with adding new variables to the data so now we can check the overall correlations by plotting the seaborn heatmap 5071, The Linear Regressor predicts negative values for SalePrice and that crashes crossvalidation scoring with our metric R2 scores would be working 25181, Building a random model Finding worst case log loss 21402, Submission 42301, Here among all the models the best performing model is estracted to perform the prediction 23028, Event Day flag and Sell Price don t have so strong relationship 1219, Fill numerical data 0 or median 26383, implementation of the Backprop algorithm 3687, Prepare Data for Modelling 2493, Embarked Categorical Value 17706, EXTRACTING THE INITIALS OF THE PASSENGERS CONVERTING CATEGORICAL DATA TO NUMERICAL DATA 7773, Bagging 35823, Pandas 17649, Random Forest 5247, Separating Train and Test Sets Again 13540, Importing Datasets 40425, let s identify the response column and save the column name as y 17679, it s time for predict 2920, Spot check Algorithms 23627, Here we explore two methods that are very simple to use and can give some good insights about model predictions These are 14921, Pearson s Residual Test 3505, Do our grid search for good parameters 4916, Handling Missing Values in Numerical Data 20431, Helper Functions 8731, Missing Data 27276, It s too slow we can speed up by binize the variable values 26430, Here I use seven fare categories 20234, Ticket 21654, Rename all columns with the same pattern 4394, Data Preperation Cleaning and Visualization 6549, Embarked Port of Embarkation 36745, Since the daysBeforeEvent feature is used for predicting after the model trained as input we seperate the 28 days as daysBeforeEventTest 10906, Grid Search for Adaboost 38109, Converting string into numbers and imputing missing values 7440, Find all the numeric columns and replace the NaN values with 0 13728, Extracting and handling Title simultaneously dropping Name 36975, Permutation Importance 18737, Tokenization 2246, GradientBoostingRegressor 11398, We have filled in all our missing data We deem Cabin a lost cause at this point and we won t investigate the Ticket column so let s drop those here 3800, Simplified features 33240, Lets train our language model 40332, Encode tags 25271, Keras Callback Funcations 37400, we want to one hot encode the dataframes 22960, Examples of hair augmentation with OpenCV 4452, Done 9968, Removing Outiliers 31361, Printing all the outputs of a cell 7768, Linear SVM Regression 42615, Setting up the network 26333, Data cleaning In summary we want to tokenize our text then send it through a round of cleaning where we turn all characters to lower case remove brackets URLs html tags punctuation numbers etc We ll also remove emojis from the text and remove common stopwords This is a vital step in the Bag of words linear model 13155, Dropping features 25773, Sex 11274, One common way to engineer a feature is using a technique called binning Binning is when you take a continuous feature like the fare a passenger paid for their ticket and separate it out into several ranges or bins turning it into a categorical variable 37887, Evaluation 28323, identifying the Categerical and numerical Variable 38108, Final Submission 40630, Reading Data 32182, Define Loss 9264, Removed 2 outliers 12414, YearBuilt 3376, Create your model 11884, Feature Engineering 40774, Helper Functions For Making Predictions 33292, Embarked Processor 168, We use a countplot which is a histogram in disguise across categorical variables instead of numerical ones 26891, Score for A5 16004 20575, Checking the missing values 4814, We both have numerical and categorical features 34398, Time Only Model 41933, We use the Leaky ReLU activation for the discriminator 43060, check the distribution of the mean values per columns in the train and test set 9679, Lower down learning rate 6656, Feature Importance by ExtraTreeClassifier 22128, The Hyperparameters that can be used tuned in XGBoost model are 20403, For each cluster s centroid embedding collect the 20 nearest word embeddings 11644, Logistic Regression 38721, DCGANs are extremely similar to FCGANs 6128, Functional 21727, The top 20 categories account for the 87 of the whole 40678, let s try for K 36 5169, My upgrade creation new features 36424, Ordinal Features 11216, Get Pre adjustment score for comparison 30417, Define PyTorch Dataset 40614, It takes a while now is time to look at what goodness it gives to us 29932, First up is reg alpha and reg lambda 10508, SibSp 27562, ps ind 03 x ps ind 06 09 bin 5919, Submission file 16110, create a new feature named IsAlone 34854, Oversampling 5065, step is to create new features by combining existing ones 13552, Crossing Embarked by Sex and Survived 39292, Export aggregated dataset 16562, Sex Pclass and Embarked 2247, StackingRegressor 9524, Submission 31221, MLP Evaluation 9031, If there is no Kitchen Quality value then set it to the average kitchen quality value 2682, Univariate roc auc for Classification 38015, that the model is fitted we can check it s most important features with the power of ELI5 11881, Data 28933, Fitting model with data 24785, Retrieve everything 22455, Density plot 3949, Imputing LotFrontage with median based on Neighborhood 24270, Observation 4827, there are a couple of them where missing values indicites None so we can jsut write a for loop for these columns 2155, One of my favourite definitions of startup belongs to Eric Ries a startup is a human institution designed to create a new product or service under conditions of extreme uncertainty 1274, Filling Age NaN 43216, Import necessary libraries for hyperparameter tuning such GridSearchCV and metrics such as precision recall f1 score 28824, Fitting the prophet 7520, SHAP values for selected rows 3575, Finding Missing Values 33675, Timestamp 32840, convert all the values to int and export for submission 23526, The target for mislabelled tweets is recalculated 11428, Linear Imputation 25521, Train and Predict 28147, Let s take a look at the most frequent relations or predicates that we have just extracted 23059, When we have our data prepared we want to split it into datasets one to traing our model and another to test it s performance And the best way to do that is using sklearn We set up a test size which is standard value for this operation usually for test we leave of data which means that for training remains It is also a good practice to set shuffle True as some datasets might have ordered data so the model learn to recognize s and s but won t have any idea that exists for example 7253, Normalize data 3312, GBDT 9088, look value counts of each category in MSSubClass 31301, Observe Prediction Time With Different Batch Size 28531, LotArea 16011, Survived 22593, Shops Cats Items preprocessing 27354, x 2667, Duplicated Features 19308, Evaluation prediction and analysis 26456, RF Predict training data for further evaluation 524, Default mode for seaborn barplots is to plot the mean value for the category 20342, The are 7 transforms try them out on a single image 24286, Gathering data 6097, Holy skew transform 36400, EDA of test dataset 26698, look at the unique states in the sales dataset 42801, Variables like life sq or kitch sq are important in prediction and because they are linked with full sq it is better to fill missing values with ratio of full sq than median or mean 15912, Passengers with title Master are likely to be children we can infer those missing age as the mean age of Master 8297, Grid Search 6170, Cabin 24138, Nearest Neighbors Model 4627, Box and whisker plots for detecting outliers 11205, Stacking averaged Models Class 5810, Feature Engineering 11229, let s convert the features into category integers 19552, Train Validation Split 40060, Confusion matrix 34108, ICMR Testing centers 1829, Blending Models 28608, YearBuilt 18961, Display distribution of a continuous variable for multiple categories 21780, I would then define some helper functions 21451, Categorical 23122, Findings As expected Sex Embarked and ticketProcessed have the weakest correlation with Age what we could guess beforehand from boxplot Parch and familySize are moderately correlated with Age nameProcessed Pclass Cabin and SibSp have the highest correlation with Age But we are gonna use nameProcessed and Pclass only in order to impute Age since they have the strongest correlation with Age the tactic is to impute missing values of Age with the median age of similar rows according to nameProcessed and Pclass 5467, train another baseline RF model to get some baseline importance 20924, We can first use a built in object feature importances 10791, I wonder now about the Parch 8354, Survival rate by the title 2517, the accuracy for the KNN model changes as we change the values for n neighbours attribute The default value is Lets check the accuracies over various values of n neighbours 34364, Lets convert our date feature to date time and do it more sunnictly 1785, Random Forest 36359, Submission 42164, Data Preprocessesing 21600, Select columns using f strings new in pandas 35920, Prepare Submission File 27883, For CA the SNAP dasy are the first 10 days of the month for TX and WI 10 days within the first 15 days of the month but always in the same order 17543, Feature importance 5332, Visualize three way split of three variables differentiating with color 8566, Deleting a Row 42329, Reshaping train test set for making ready to go through modelling purpose 21036, After applying Grid Search we found the optimial n components is between to In this case we pick the mean which is 12005, Elasticnet 20841, we want to drop the Store indices grouped together in the window function 18217, Parte 3 Rede Neural Convolucional 21475, Here you can notice that we have a problem some of the generated data is very long compared to the original data 34822, Optimizers and Annealers 42734, The mutivariate kde plot of not repay is broader than one of repay 19312, Evaluation prediction and analysis 13555, Ploting Fare Distribution 24121, Use macro model predictions to adjust XGBoost micro predictions 11182, Save Cleansed Data to disk 1883, We change Sex to binary as either 1 for female or 0 for male 13043, SibSp 29825, Trained FastText 25494, Label encoding 38198, Random Search looks like grid search but without testing all possible combinations we chose only a limited number of combinations drawn randomly without discount and we evaluated our model with what we have drawn 12109, Once again dealing with missed MSZoning feature 666, Random Forest 2164, Our initial approach to estimate Age missing values was to fill with a placeholder value 29372, Creating submission file 41034, Explore non WCG females 16788, Importing and Merging Data 2351, Gradient Boost Blend 6473, Evaluate Ensemble method 31778, This is just an example 957, Feature importances generated from the different classifiers 40202, Sample predicted Images 43036, Split the Data into Train and Validation Set 8931, MSZoning 16588, from C high number of 1st Pclass people Survived lets fill C in missing value 38566, Ridge Regression 36081, Model 12707, There are two missing values for Embarked let s replace it with the most frequently occurring value 30632, Relationship between age and survival rate 32792, K fold CV with Out of Fold Prediction 11043, Train the models 6100, Build the Model 21220, parameter tuning for getting Global maximum and this is done by tuning the Learning rate 5597, OverallQual 11938, Transforming the target variable to log values so that the error is equally impactful to the low and high prices 20233, SibSp Parch 1800, Observations 24596, Again zeros and ones are not mixed up 35424, use the convolutional neural network architecture to train the model for this we need to modify our data as 984, Extra Blending made easy 29779, Adding Noise 4786, OverallQual and GarageCards have a positive correlation with the Sale Price 5060, How do neighborhood and zoning affect prices 14618, Station 4 Transforming and scaling the fare 10107, encode a categorical value in Test Data 580, 19 attributes have missing values 5 over 50 of all data 12420, We inspect 3 example ML models RandomForest KNN and LGBoost 12940, Do you have longer names 2414, Categorical Imputing 3494, Best score 22283, Prepare Data 38819, we setup the RandomForrestClassifier as the estimator to use for Boruta 32132, How to find the most frequent value in a numpy array 10041, Determine outliers in dataset 6136, There is no other options 25419, I thus use the date column to group rows 4101, Data Marge 35688, Catboost 9928, I wrote the next lines when I was coding in after a previous study of the features 1093, Embarked 21240, create our submission file 22080, Body of script change number of patient to create other video 24012, Accuracy 11558, check how many features have we gotten rid of 43335, Here is the brief introduction about all the packages from their documentation page 32858, Checking for outliers 40076, Multi colinearity Categorical 9748, Embarked 19248, Fitter 7975, Quick completing and converting a numeric feature 42788, Word Cloud 625, Just about formally significant 31505, We may have to implement the same process for all the features to check how each feature correlates with the target variable which can be quite tedious given the number of features 15561, there is both a Miss and a Ms title Without much considerations I m joining the Ms titled 16539, convert the Sex column from categorical to Numerical use the map function for this 41415, Load initially explore data 18685, let s use a list comprehension to generate the labels 16341, Perceptron 7868, I explore both PClass and Sex in the same plot 19964, Feature importance of tree based classifiers 27841, Epochs and batch size 31288, Prophet appears to output very similar shaped predictions to ARIMA 23840, Taking X118 X314 and X315 4773, Logistic Regression 40832, With weather 1 2 and season 2 3 and working days the bicycle rental count is maximum 783, Ridge 36227, let s visualise the images in our dataset 8398, IMPLEMENTING TPOT 9411, Cleaning Filling NAs imputation font 34962, Exploratory Data Analysis 13331, Fare completing feature with its mean value div 7656, We use multiple regression models Lasso learn org stable modules generated sklearn linear model Lasso htmlsklearn linear model Lasso Elastic Net learn org stable modules generated sklearn linear model ElasticNet html Kernel Ridge learn org stable modules generated sklearn kernel ridge KernelRidge htmlsklearn kernel ridge KernelRidge Gradient Boosting learn org stable modules generated sklearn ensemble GradientBoostingRegressor htmlsklearn ensemble GradientBoostingRegressor XGBoost and LightGBM To find out the best parameters for the models we can use GridSearchCV learn org stable modules generated sklearn model selection GridSearchChtmlsklearn model selection GridSearchCV 42785, Submission 22146, Generate test predictions and preparation of the submission data file 39105, Printing keywords 35781, Compare different predictions to each other 5251, Creating the Feature Importance Dataframe 14264, Linear Support Vector Machine linear SVM 22577, 0s 729, Funnily enough LotFrontage gets dropped That teach us next time to spend so much time data engineering Joking aside it looks like a lot of dummy variables get removed 28094, Label Encoding 21776, FINAL TEST 35375, Train model 8243, Grid Search CV 41697, Well some places are visited at certain time periods for sure but can t do much else until we disentangle time 6279, As expected from our analysis of Parch and SibSp Family Size follows a similar pattern 17774, The best survival rate is for passengers embarked in Cherbourg the worst for passengers embarked in Southampton 15104, Exploratory Data Analysis 32178, compile data processing 13962, Pclass 13483, Reference 244, Library and Data 10774, Because I m using a SVM model in my ensembling I need to apply some standard scaling to my data the following code use a scikit transformer design for this effect and apply it on the data 10981, Testing to get best possible accuracy 3427, drop the Cabin and Cabin M variable for now since we ll focus on Deck instead 1561, SibSp 11532, Submission 5313, Model fitting 21548, Training and evaluation of the model 18303, Feature Matrix Creation 22180, RoBERTa span 20631, Lets visualize the top 10 stopwords and punctuations present in real and fake tweets 41939, Generator Training 16901, New Feature NameLen 24235, This is the augmentation configuration we use for testing Only rescaling 7382, The matched values of WikiId are presented in the list wiki id match and are added as an extra column to the DataFrame kagg rest4 corr which is a copy of kagg rest4 28, Weight Average 40314, Target 10896, Passenger Id and Ticket are just random variables and they do not give any intuition for their relation with the chances of survival of a passenger 14690, The Age variable is missing roughly 20 of its data 11254, Data Preprocessing 15527, Age 42211, Define model architecture 25942, LightGBM 34825, Model Fitting 14381, Replaced all available values with 1 and all the missing values filled with 0 in new feature CabinBool 9702, Missing values in numeric features 22184, Remove low prices anything below 3 16125, Comparing Models 22399, antiguedad 8003, Train Elastic Net 5992, Sofar 22517, Hence this technique is more like oversampling but here we oversample BOTH classses rather than just one 17796, Extract Deck from Cabin 14799, Gradient Boosting Classifier 42372, Submission 6930, Missing values in column LotFrontage I fill with median values 28819, Mondays and Sundays present best average sales and Saturday is the weakest 21534, Scaling 7677, Numerical features 40308, also check the correlation between the three fields 8553, RANDOM FOREST ON TEST DATA 37363, Age features are very important 3776, Last Name 2175, The plot suggests that those who survived paid a higher fare 16557, Submission 26479, Create dataloader for mini batch training 24913, Time Series in China 16604, Continuous variables 27473, Bag of Words Countvectorizer 21138, make big cross over and combine 35784, use different function to calculate ensembles 42773, Fill 3867, Categorical Data Pipeline 12061, Ordinal Variables 1198, Feature Selection 829, Checking correlation to SalePrice for the new numerical columns 33867, Preprocessing for Modelling 20184, Glimpse on the data 17764, Go to top font 39718, The embedded vectors for a specific token are stored in a KeyedVectors instance in model 8227, After shortlisting the variables I built a pair plot between the variables which have either a high positive correlation or a negative correlation 14764, Using 75 25 Split for Cross Validation 27005, Embeddings 5117, Variable Correlations Only Numerical Fields 20104, Item count mean by month shop for 1 2 3 6 12 lag 19649, Wrap the scoring in a function to try different values for prior weight 29945, First we need to format the data and extract the labels 14361, Feature Description 5056, Since most of the house were built during the two decades before 2010 let s take this into account and analyze the actual age of the house in the year of sale 14806, Outlier Detecetion 33739, Set the threshold value for predicting bounding box 11272, The coef method returns a NumPy array of coefficients in the same order as the features that were used to fit the model 15244, Generation 14738, It was that easy let s do our evaluations 16565, Checking for missing values 28546, Model 36631, How about price Maybe there are some outliers and skewness 13701, we ll deal with the missing values 5043, get more detailed stats about the distribution 41607, Reshaping the data 27661, Evaluate the model 26872, apply the first layer filters to our selected image 31229, Features with values between 10 and 10 2278, K Fold Cross Validation 16916, Check feature correlations against each other 19387, Encode categorical features 508, Support Vector Machines 37813, Only a few tweets are missing keyword 28145, Relations Extraction 39213, Clean up 36577, Gender and age statistics 629, Similar to the known Cabin numbers what about the passengers for which we know the age 24446, Cleaning the text 28106, try something different We ll make a new category for every categorical column as NA 8965, group by family 5082, we ll look at the predictions in detail 35829, If you want to delete any features this can also be done easily with pandas 8534, FEATURE SELECTION REGULARIZATION IN REGRESSION 16774, Prepare Submission Data 32681, Few specific aspects of this exercise demand special consideration p 29852, Fine tune The model 20653, SCATTERPLOT TO ANALYSE RELATION BETWEEN EACH PARAMETER AND THE OUTPUT 24806, numeric data 21150, Important remark This operation be a bit surprising we have to split our train data set to differentiate train and test for modeling The so called test set from data doesn t contain target so we would be not able to make evaluation on the basis of this 22708, Loading the Dataset 18254, Model 28396, Split into independent and dependent features 29167, Electrical Fill with the most frequent class 22343, CountVectorization 25460, write a submission file for each submodel 25378, With an annealer we can set a relative large rating rate in the beginning to approch the global minimum fast then reduce the learning rate by 10 every epoch to find the best parameters smoothly 18514, Load data 7436, Grid Search 19137, Plotting violin plots of weights to performm a sanity check 34839, Since XGBRegressor model is the best model let use this for prediction 19590, shop and main cate id 20103, Item count mean by month item for 1 2 3 6 12 lag 439, Utilities Since this is a categorical data and most of the data are of same category Its not gonna effect on model we choose to drop it 1955, Training by XGBoost algorithm with default Parameters 26356, Feature Engineering 11547, A lot of the categorical variables have rare labels that appear so little 36568, Prepare data 33198, Recompose pixel digits to image data 42858, Optimization 39287, PRICE FEATURES 19398, Save the model 16621, Modeling 34823, I have set the epoch to 10 for the purpose of kernel 15178, Data exploration 16744, voting 41622, combine training test data to save data cleaning preprocessing efforts 19585, date block num 1610, Target Encoding 26640, Submission file 25476, We can get a better sense for one of these examples by visualising the image and looking at the label 2940, check the missing values 482, Fill missing values 10214, It s quiet evident that number of male passengers are almost double of female passengers 26319, A Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap Aggregation commonly known as bagging 6152, Split train set into two subsets 19630, There is a small but still significant difference in the distribution of target 1 27281, Prepare for submission 14586, C Cherbourg Q Queenstown S Southampton 17929, Load Dataset 21551, for visualization 18470, Sales 26514, When we re happy with the outcome we read test data from test 18724, get the filenames in the test folder 17361, now look nice 24410, Submission File 27455, Lowercase 18033, Everybody 27461, Stemming Lemmatizing 7441, Verify that there are no null values in the data set 27353, diagnostics is not bad so we are good and continue 24051, Encoding nominal categorical features 32770, Evaluate The Model 24931, Date and time 16304, NOTE 28424, Outliers by price and sales volume 21084, The unique value of ps car 11 cat is maximum in the data set is 104 30900, means the knn could not find a suitable city id for that pair of latitude and longitude 33993, Logistic regression is good at classification but when complexity increases the accuracy of model decreases 139, Using RandomizedSearchCV 15844, Cabin 3275, Fill all other Basement features missing values with None or Zero 22352, Using xgboost XGBRegressor to train the data and predict loss values on the test subset 28578, TotalBsmtSF 5506, Creating a feature based on Sibsp and parch 11634, Suport Vector Machine 14080, Logistic Regression 34474, PCA 21903, If you got this far please upvote span 37305, Inverse Document Frequency 28105, Remove the SalePrice and assign it to Y 29142, Feature importance via Random Forest 19191, Predicting using the Simple Linear Regressor 7620, ElasticNet 10810, That s a lot of categories 26054, We re finally ready to train 19446, Model 2B 3997, measure the RMSE for sklearn 13045, Tickets are of 2 types here 36898, RMSprop 25647, Run the next code cell without changes to obtain the MAE for this approach 2153, Startups use pitches to sell their idea 30272, Applying web scrapping 17723, Individuals who embarked through port S paid the lowest fare 16903, Ticket 1021, Fantastic now let s insert these values into our Random Forest Classifier 41620, Good 26522, TSNE Visual Clustering 20057, Prepare data for model 40453, NeighborhoodBldgType 6161, Prediction for sklearn models 41871, Randomized search 6695, Submission 28430, Item name category feature additional feature 19062, Loading the test set 32501, Processing the Predictions 24476, Plot the first 9 images in the test set 25184, XGBoost 11185, Import libraries 23797, Balance Methology 1391, Give me your feedback how can I increase this model 42996, Logistic Regression with hyperparameter tuning 11394, find the missing fare in the data 16915, Check Each feature against Target label 38657, Gender Distribution 1149, we are going to pick some features for the model 38271, Firstly we define vocabulary size as len test That means this system here support len test different words 27485, We only used a subset of the validation set during training to save time 23383, The next functions load an image and the corresponding bounding boxes depending on randomly picked image ids 27324, Learning schedule 15746, Cleaning Data 32266, Relationship between variables with respective to time Represent in horizonatal line 23252, We build our neural network model 13439, After explored features in dataset 23453, Day 18285, Category and Number Lists have now been changed 10928, Draw random graphh with nodes and probability 26770, Detecting best weight to blending 13858, so we have titles 22605, Months since the first sale for each shop item pair and for item only 11797, Missing values 6412, Check variation in the feature values 23416, And now we can train the model 42272, Make day month year column to analysis whether there is meaningful difference between each interest level values 20435, Encoding the text into tokens masks and segment flags 7911, These scatter plots give us some insights on a few outliers e 13276, Encoding categorical features 29015, Distribution of Pclass feature by Survived 10577, Machine learning in pyspark 11473, ANN Artificial Neuron Network 41676, Resizing the Images 7226, Pool Quality Null Values 20477, now analize the application bureau train data 14592, Age Group 7931, Elastic Net 41269, Qualitative Colormap 12515, Woah The first feature we engineered did end up being pretty important Way To go 11609, Take care missing data 18384, Here is how to extract keyword location text to replicate the structure of the dataset used in this competition 21182, Housing Prices 15371, Fare Feature 24800, CNN 12368, SaleType 42044, Replace object column to integer column 35061, Augmenting an image 6923, Make Submission 19736, Observation 35494, For example let s look at first sample pixel values 23443, Heatmap of all the continuous values in the file 27767, Submission 8411, However by making the year of construction of the garage an indicator of whether it is newer it becomes easiest to identify a pattern of separation 947, Tune Model with Feature Selection 12917, Types of Variables 8510, Feature Engineering 31167, title 35458, Visualize the Chunk of Melanoma Images 9158, This narrows down the features significantly 38690, Before Mean Encoding 15632, Survival by FareBand and Gender 20447, application train 29730, I need to give bounds for these parameters so that Baian optimization only search inside the bounds 16177, we can safely drop the Name feature from training and testing datasets 37456, Fitting the model 23562, It looks like full all is not the exact sum of male and female population 38297, We saw there are only numerical columns in this dataset 37636, It is always a good idea to tune the Dataloader s num workers parameter From the PyTorch documentation 25952, Departments csv 29944, Implementation 35116, Creating Training Pipeline 22840, Ran out of memory when trying to create a dataframe from january and february lists 26723, For WI 21527, take a look at the downsampling operations of a multilayer convolutional network 28266, we shall take a look at categorical features 27121, MasVnrType 11489, SaleType 6516, Outliers 15276, Importing Libraries 11496, Check Correlation 6989, Size of garage in car capacity 18108, After creating the submission I always check the format and the distribution of the test predictions 17675, Miss Mrs Master have a lot of chance to survive compared to the title Mr 208, Report 15248, Tune 15501, Age 9206, Gender Distribution by Ticket Class 20162, We are not passing parameters in this step to keep it simple and be using the default ones 30082, I found passengers which joined with their family or alone by picking up counts of sibsp and parch 35481, TPU detection 42940, Loading the data from feather files 38679, Categorize Image Size 36749, LSTM Model with Keras 30516, How does Target Varies with Suite and Income Type of Applicants 14325, Fare 35440, Reshape 29074, Categorical features mean encoding 35783, Stacking Averaged models Score 2529, XGBoost 9307, The error here is that our encoding forced an order among the categories It is saying to our models that BrkFace None Stone and this can be very harmful to both the performance and the explainability of the model because that ordering does not make any sense 32361, Missing Values 14662, Encoding Sex PClass column 17674, Categories of title are a good feature 20585, Applying Feature Scaling on Test Set 17793, Build families 38221, Some points are misplaced 42635, The Human Freedom Index 18400, Target 839, Creating Datasets for ML algorithms 20535, Define error metrics 14396, we can drop the name feature since it contains no more useful information 26948, Normalized information about building where the client lives 36051, Functions 20120, Hyperparameter Tuning with Optuna 7268, Embarked Feature 18677, Evaluating Ridge model on dev data 6545, i am going to plot a heatmap for just fun so if you don t understand it go ahead even i don t understand this below heatmap 8466, Select Features by Recursive Feature Elimination 26916, Lets solve the problem of outliers 37468, EDA 21742, Highly skewed item cnt and item price 42936, Getting the list of names of the added features 2023, Stacked models 1969, KNN Classifier 42021, Concat Merging and creating a new data frame 6401, Preprocessing Train File 14172, Here we display the features with their corresponding importance values based on each model 26961, Anual Sales 42648, target 0 1 9749, Fill the Missing data 22160, Column extractor 32247, we fit for 2 epochs 13338, Another piece of information is the first letter of each ticket which again might be indicative of a certain attribute of the ticketholders or their rooms 6181, Data still evenly distributed 35064, Making predictions using Solution 3 15566, A feature for the cabin deck 11885, Logistic Regression 1395, Correlations between numerical variables and Survived aren t so high but it doesn t mean that the other features are not useful 24503, Generate the output 28946, try to optimize the hyperparamerts using Randomised search 6471, Try on the test data and make submission 4805, learn org stable auto examples preprocessing plot all scaling htmlsphx glr auto examples preprocessing plot all scaling py 34368, Modelling 25470, Define architecture 8432, Check and Input Basement Features Nulls 22601, Last month shop revenue trend 8730, Scatterplot with Correlated Variables 17727, The reason q 4 was used is becaused the data be divided according to the values that 40865, Model Evaluation 35334, Data Augmentation to prevent Overfitting 12982, Sex 4454, Plotting and visualising the distributions of different variables 18884, Fare and Embarked are two columns where test and train differ in missing values 5322, Display heatmap by count 1967, Random Forest Classifier 30401, Defining the architecture 7727, The BsmtFullBath FullBath BsmtHalfBath can be combined for a TotalBath similar to TotalSF 41276, Transparency 36747, Here is the important part 11146, separate into lists for each data type 38756, Gaussian Naive Bayes 31397, Lets create our model make sure to add the dropout value so that our model does not over fit 38782, Logs of model predicted prices 15732, Confusion Matrix 23120, Impute Age 25288, Now let s try with the optimal learning rates 36617, Plot FacetGrid using seaborn 2797, Deploy Trained Model on cloud 6435, Conditioning The Coolest Feature 16465, Feature Selection 27496, Shallow CNN with no regularization 7009, Value of miscellaneous feature 1837, 1st and 2nd Floor Area 31904, Compile the model 12684, Every column that is either an int or a float can be described 6322, Adaboost 5346, Diplay actual as scorecard with increase or decrease number 37053, Looks like Cabin is alphanumeric type variable with no special characters between letters and numbers 254, Library and Data 10916, Select features 20108, Item count mean by month sub item category for 1 lag 27048, Visualizing Images with benign lesions 6280, Sex 32559, Age 23118, Imputing Missing Variables 6655, The positive corelated attributes are age age bins SibSp Family size Parch Family size Sex Title Survived Sex 13142, NameLength as Survival function 9768, these are the features I use in this run 20177, Converting Images to binary 10043, Encoder 32319, Predict the scores using KNearestNeighbors 3747, isnull 23407, Scaling 4625, Using histograms we can easily check the skew for each variable 40484, Gradient Boost Regressor 105, Hypothesis testing for Titanic 42946, Creating the submission file 3547, now get this awesome new function to work on other features 18376, Fit and Score Model 40334, Baseline 3909, if you take a look at the discription of the variables you notice that this is actually not missin data it means no item is found no pool is found for example and so on for other variables so we replace these values by None 35419, Training data Extracting from dataframe converting to list numpy array scaling into range 35905, We unzip train and test directory using zipfile library 9292, Statistical Modelling 23674, Split into training validation sets 32850, Data cleaning 17927, Interesting question The test accuracy score is 79 31535, 50 values are close to mean value so replacing with median 634, Travelling alone appears bad enough to be significant 35342, Making predictions on the Test Set 31845, Category dataset preprocessing 11067, Adding some categorical features 30833, Use the date feature to get the year month and hour features 29712, Visualizing distributions for numerical features is essential 6821, We are going to use regularized linear models to avoid the risk of overfitting 42966, I converted non ordinal categorical variables to dummy variables 4422, Make Submisison 11516, Random Forest Regressor 14099, SVC Output file 24669, Modeling 9005, Convert categorical nominal variables into multiple columns using One Hot Encoding 13252, extract title 42344, Convert categorical variables and process real test data 25800, Besides it looks like there is a strong correlation between the number of entries of the contributors in both datasets 8834, SUBMISSION 4540, Matplotlib 7933, Random Forrest Regressor 25858, BagOfWords Implementation 36228, Here we have used train test split from scikit learn to split our training images and labels into train and test data Since we have our test size set to our initial dataset be split into training and test data elements 26898, Using Cross validation 8967, sibsp parch 7537, Members with head count of more than 6 never survived in our train dataset so lets make 6 members that is 7 and 10 members as 7 members 2077, Following the best practices let s calculate our own CV score that be used as a reference 5834, Feature Engineering 15625, Passenger Class Feature 3199, Transformations 4190, We exluded from this list the Id column which is an unique identifier for the houses 16377, Before combining features all features needs to be changed to numeric types 12633, Text Based Features 4353, make new feature of OverallCond OverallQual 21016, Bar Chart 26301, Test on test datasets 24859, let s check how we are performing on two groups 31230, Features with max value between 10 20 and min values between 0 10 2286, F Score 6860, Numeric Float Into the Battle Field 19069, Get the probabilites for the test set and create a list of the probabilites for each image in the test set and convert the probabilty to a float 24723, PPS Predictive Power Score 18032, Errors Analysis 9080, I wonder if Condition1 and Condition2 are ever equal in values 27760, Tokenzing the text 31526, We repeat the same process as before 8837, The author also says I would recommend removing any houses with more than 4000 square feet from the data set so let s do that too 5507, Fare Category 10369, Categorical Variables 27958, One hot encoding 26732, Plotting monthly sales accross departments 17044, Frequency encoder 17944, Filling Data fare 4173, Domain knowledge discretisation 17894, Lets visualize the effect of covariates on probability of survival 27263, Discrete variable 42889, Hospital Bed 1138, Model evaluation based on K fold cross validation using cross validate function 8884, The 5th Feature that we make is the overall average rating of the house to determine its price We do that by taking the arithmetic average of the OverallQual and OverallCond 18487, We still have StoreType Assortment and StateHoliday as Obejcts we need to convert them to numerical categories 17829, Sex is the dominant feature followed by Pclass and Fare 3397, BsmtQual BsmtCond BsmtExposure BsmtFinType1 and BsmtFinType2 For all these categorical basement related features NaN means that there is no basement 32364, Scans by Anatom Site 19799, Mean Median Mode Imputation 37421, first take a look at the class distribution of sentiment label 4813, Dropping unnecessary columns 22750, Import Libraries 12636, also define a function for removing outliers that be used later on 26642, This table means that 3M doc have meta infor 22387, GradientBoostingRegressor 37544, Data Preparation for Keras Model 11767, Our stacked model scored an impressive 42376, CNN 9603, Data Vizualization 4100, New columes 5909, u AdaBoost u 973, Set up our dataset preprocessing 4577, Fifth step Modelling 37830, Consider as cut off for consider probability as 22088, Import required libraries 18081, look at some examples of images with small areas covered by bounding boxes 36538, 200 6777, The Chart confirms Women more likely survivied than Men 8527, Basement Features Continued 37626, Here we split the train data to create a validation set 8077, The rest can be safely imputed with 0 since this means that the property is not present in the house 24796, Wavenet 26191, HyperTuning 40242, before we do any kind of analysis we must check the kind of features we are working with what percentage of those are null 10766, to tune all the hyper parameters 3525, SalePrice Correlation matrix 25436, Label Encoding 25834, Calculating and analyzing Char length of each text 38430, Submission 42463, ps car 12 and ps car 13 27123, If we look at the types of masonry venner and their corresponding area 17784, We apply the rule for extracting the title 39886, Scaling 33848, Basic Feature Extraction 24030, One more thing that we may notice from spikes is that sales count depends on days passed after release date 10217, Take Away Points 26336, Models 7544, our data is ready now its time to use it for model building and prediction 33482, Observations 43283, Instancia uma nova RandomForest 13047, Cabin 37197, Box Cox Transformation of highly skewed features 33277, Observations 13723, Perform OHE on Pclass new Sex Embarked and Title 10504, Fill the Age with it s Median and that is because for a dataset with great Outliers it is advisable to fill the Null values with median 4456, Comparing survival rates among different variables 23433, Romoving Emojis 32514, Extracting VGG19 features for training and testing datasets 4550, Treating categorical and numerical features differently 21505, Image with the largest width from test set 11340, Family Size Feature 35101, Compute accuracy of model 10975, Top influencers 12145, Applying cross validation on all the algorithms 2 36667, As we continue our analysis we want to start thinking about the features we are going to be using 8125, Submission 42560, Function to search for best threshold regarding the F1 score given labels and predictions from the network 26559, Submission 16370, Categorizing the Age values 25463, and now to the Test Set 19528, Creating User Defined Functions UDF 31435, Before submitting run a check to make sure your test preds have the right format 423, Target Variable Transform 35650, Baseline models font 19192, Predicting using the DecisionTree Regressor 11040, Prepare our data for the pipeline 11315, Age 19453, First Hidden Layer 4629, Scatter Matrix Plot 22098, Avg Accuracy VS Number of Epochs Graph 7889, I would simulate the training using the new features 22077, Positive values in the column Diff Jaccard pred vs text mean our prediction is better than simply taking text as selected text 11622, See how each feature are correlated 33803, This creates a considerable number of new features 14634, let s write some functions to help clean our data 9730, One more variable of note is GarageCars 699, If we now select survivors based on the new probability threshold we finally obtain a nonzero number of survivors 24863, Just need to do this little trick to extract the relevant date and the forecastId and add that to the submission file 15257, As Most of the records embarked at port S Filling missing values for two records with values S 21660, Convert numbers stored as strings coerce 550, SVC features scaled 30761, Display distribution examples 15098, Additionally from looking at the features it looks like we can just drop PassengerId from the dataset all together since it isn t really a helpful feature but rather simply a row identifier 4917, Handling Missing Values in Categorical Data 27492, SAVING THE TRAINED MODELS 6251, Embarked 3771, There is no missing value on this feature and already a numerical value 31434, Check on some random input data 23066, You should notice that your model training ran about twice as fast but the accuracy change was trivial 11539, GradientBoostingClassifier 18731, The same patterns appear to be present a dropoff in sales after substantial returns 34868, I define a function that checks the intersection between our vocabulary and the embeddings 22753, R for Covid is estimated to be between to Source Forbes Report covid coronavirus disease may be twice as contagious as we thought cbca 41089, We use StratifiedKFold to split our data into 7 folds 28851, GPU 12310, The higher the quality the better the selling price 18085, Plot the brightest images 16094, Heatmap of Correlation between different features 14181, Importing Libraries 14394, we can drop the Age feature from the dataset as we are using AgeGroup now 15450, We still have some categorical features which we must change to numerical so we use a Label Encoder 34461, WI 3 35471, Visualiza the skin cancer atypical melanocytic proliferation 507, Nearest Neighbors 26585, TASK 2 IMPORT LIBRARIES AND DATASET 2371, Evalutate LGBM Stacker 21424, LotFrontage 41254, MODEL TESTING EVALUATION SELECTION 21041, Related paper Topic Modeling and Network Visualization to 32394, Here we set config for our next steps 29742, we got 0 23681, we can instantiate the model and take a look at its summary 21598, Create new columns or overwrite using assing and set a title for the df 42331, Information of ReduceLROnPlateau can be obtained from here 30751, Hyperparameter tuning for best model 40045, Both have very similar target distributions now 40428, Ensemble Exploration 27192, XGBoost 40733, Visualizing All Intermediate Activation Layer 37619, attributes of which have less than 10 unique values do One Hot Encoding 7352, Correlation 37871, Distribution of dependent variable 9829, Creating Dummies Variables 28469, Plotting columns Latitude and Longitude 38065, NLP Features distribution Disaster Tweets vs Non Disaster Tweets 2409, GarageYrBlt MasVnrArea and MasVnrType all have a fairly decent amount of missing values 40487, Ensemble VotingRegessor 9815, Survivals Survived 1 or died 0 35308, Start training 37232, CNN Model 16038, We must fill Fare NaN value with median of Fare 4134, Scaling the Data 652, And that s quite expensive for a 3rd class ticket 30108, PATH is the path to your data if you use the recommended setup approaches from the lesson you won t need to change this 3699, Predict 8361, Embarked fill embarked with a major class 18121, Pipeline Validation 2352, Logistic Regression 30655, To increase iteration time I create a table grouped by keywords 12089, Fit the first part 18626, For Fare 27656, Normalization 11491, Exterior1st and Exterior2nd 37323, Select pooling layer pool size parameter 39992, Creating Dummy Variables 2897, Define Performance Metric 12403, Exporting output to csv 4194, The first plot is an histogram with the SalePrice median value of each Neighborhood categories 7966, have fun now with Machine Learning and the Regression algorithms 712, Perhaps we should impute medians e 28298, Load best model evaluated at validation set 1852, Box Cox Transform Suitable Variables 40792, Check if it s a class imblance problem 7309, Observation 8120, KNN 18628, Applying RandomForest 12694, Fare 26918, The two outliers are the one with 7 31820, Confusion matrix 14221, to create features feature vectors that make machine learning algorithms work 27417, More Training data 22660, Evaluation Functions 27549, Display interactive filter based on click over bar 1006, let s bundle them 26758, Submission 9589, Viewing the tail of the data 20230, Age 13981, Map Title to numerical values 22616, Explore Apps 23317, Add previous shop item sales as feature Lag feature 32233, We also need a way to evaluate our model s performance for classifying the numbers 24141, XGBOOST 28506, We can make individual predictions for numbers to test our model is it working or not 11800, let s handel skew in all numerical features 29872, evaluate our model 26908, Fit the model 18023, Woman Child Group fates are connected to their class 35622, Define our student network 5849, Observations 18622, Data Preprocessing 11356, Log transformation of the target variable 16876, Embarked vs Survival 10825, I thought it be less 16858, Person 10394, Gradient Boost Regressor GBM 22744, Model 26374, Fitting the network 7154, We already know our datasets dimmentions now let s take a first look into our datasets structure and possible missing values 35390, Training Fine Tuning and Validation 11536, Continuous Variables 41435, It be more challenging to parse through this particular item since it s unstructured data 22598, Traget lags 28789, the first two common word was I m so I removed it and took data from second row 5490, summarize what we have done till now 9058, GarageFinish 12143, Training the model 2 20824, Join weather state names 5341, Diplay quanitive values of a categorical variable in funnel shape 16700, Binning data into different categories 17884, Deployment 29926, we can look at the instance of is unbalance a hyperparameter that tells LightGBM whether or not to treat the problem as unbalance classification 10425, Logistic Regression 16186, We can create another feature called IsAlone 11486, MSZoning 2356, Radial Basis Function RBF 7828, Make predictions 5409, Obviously most groups of female are more likely to survive 39214, A really simple neural network 6832, Categorical Features 35597, Greedy Ensemble 22223, Normalization Normalize etme 20130, Cross validation 27533, Display the variability of data and used on graphs to indicate the error 36068, Generate Output 18952, Display distribution of a continous variable 29457, Gaussian Naive Bayes 20591, we are done with all the models 8760, Survival By Sex 30851, Location for occurrence of Other Offenses 35827, The HeatMap visualization is from this kaggle kernel 5 on leaderboard data 10300, Initial Analysis and Feature Processing of Primary Training Data 1164, Feature Engineering 2401, Using ColumnTransformer to manipulate different columns 15618, Mother 1694, Get to know your dataset using Pandas Profiling span PandasProfiling 20413, now construct a few features like 17959, I choose Gradeint boosting model 28402, Simple Imputer 18339, scatterplots are a good way to visualize replationships between quantitative variables 25274, Validation Accuracy 22356, Wrinting the is and loss values to submissions 480, Before we go forward let s make a copy of the training set 36123, Ordinal categories features Mapping from 0 to N 18284, Made conditions sets that were responses found in the data 34787, There is no line plot for weather 4 because there is only three data point for weather 4 23578, Before running the hyperparameter search define a callback to clear the training outputs at the end of every training step 16023, Name Ticket 15850, Fare 20695, Model Architecture Plot 38700, Probability of melanoma with respect to Anatom General Challege 1888, Feature Rescaling 1867, Find useful interactions 36426, Ordinal Mapping 8609, Perform One Hot Encoding to all nominal variables 36563, Ensemble Model 15156, Extracting titles from name 27833, Reshape 40734, Creating submisson 39003, Initializes parameters to build a neural network with tensorflow 40979, Data filtered out only for store 36 24117, Combining 13997, Create a new feature FamilySize from SibSp and Parch 91, It looks like 35181, Adversarial Validation 12254, Scikit learn Implementation 20291, Survival for women of class 1 and 2 is ALMOST 100 42065, Renaming Columns 4953, Notice that we are using cross validation technique with 5 folds 39840, Statistical Description 13444, Parch don t have big effect on numbers of survived people 17413, Correlation Heatmap of the Second Level Training set 27897, Summary of the Model 28572, BsmtFinType2 22915, A little improvement from the earlier model 36564, Blend Models 4509, Missing Values 4265, Exterior1st 43148, Visualizing images 4278, BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF BsmtFullBath BsmtHalfBath 43151, Downloading the pretrained model 6399, HeatMap 5976, LogisticRegression 7234, Finding the optimum Alpha value using cross validation 32136, How to compute the row wise counts of all possible values in an array 31710, Load the weights 9196, There is a way to split the string values easily but the information gain from the 41 examples in comparison to the roundabout 1000 missing values are pretty low 37774, let s run the code under the control of the memory profiler 5994, Correlation Matrix SalePrice 35851, We refine the edges of the numbers to be clear using unsharp filter from open cv 486, Defining our scoring metrics 38267, Removing all the emojis 38684, Age 6924, Separando as vari veis para aplica o do modelo 24223, Apply the Estimator which got from parameter tuning of Random Forest 4868, Filling NAs 5579, Xgboost Regressor 19837, Yeo Johnson Transformation 18695, save our model s weights 37984, Because I didn t find gridsearchCV and randomsearchCV that can be used with fit generator I have to define my own sudo randomsearch 21236, Deciding our evaluation metrics 32904, Example of preprocessing 16964, We fill the Age missing values with the median 28 28320, identifying the Catergical and numerical variables 34421, Common words 41711, 3D plotting the scan 20798, LotFrontage is correlated to Neighborhood so we fill in the median based off of Neighborhood feature 9621, train test split 14608, ADABoost 6382, Bar charts are commonly used to visualize nominal data 6591, OOB error also called out of bag estimate is used for measuring the prediction error in random Forest 34755, Punctuations 24835, use k means 17967, Dedicated datatypes for booleans with missing value support data type with missing values support 733, we might have juts gotten lucky by stumbling upon these two outliers 3963, Split the data into train and validation set 31839, Making it flexible 14321, Age 23811, Piping ML Methods 29907, Data Augmentation 33660, Create a submission file 19641, Some device models can belong to more than one brand 10914, Transform skewed numerical data 22870, Data Augmentation 15494, Dropout 8997, Categorical feature importance 29170, SaleType Fill with most frequent class 12830, Fare Analysis 40291, Acctually it is another step that you can skip but to make it more clearly I am gonna make it explicity that our classes are dog and cat 24254, Embarked 25729, Load datasets 8518, Linear Regression Model 23437, Baseline Model 26365, TSNE or UMAP Visualisations 16586, Finding out missing values in Embarked column 31889, Bagging Classifier 22638, Model 3 January Average 2013 2014 2015 2361, Class Predictions 26547, Fit the model 7483, Mapping the titles depending on survival rate 38618, Surprise again 24052, CatBoostEncoder replaces a categorical value with the average value of the target from the rows before it 33785, now look at the number of unique entries in each of the object columns 4195, Other Important variables Garage variables 506, Logistic Regression 5615, Modeling 14578, Test data overview 1120, There are only 2 missing values for Embarked so we can just impute with the port where most people boarded 26537, it s time to put the network to test note on theano can take 1 3 mins to compile 886, Train again for all data and submit 13715, 2nd METHOD BASED ON PCLASS 39733, Something else that just stood out to me is that I m not quite sure about how important encoding variables is 40968, Dropping non useful column 27113, Missing values in test dataset 19162, If brand name is present yes no features 29781, Original Images 25023, Zero centering 9604, Survival Details 10261, First the coding helper function stolen from 39964, Feature Engineering 32321, that I have checked the accuracy precision and recall I predict the scores test 1676, comes the cool part we can calculate the accuracy metric ourselves Do you remember the definition Let me remind you 6281, Ticket 27059, Make Predictions for Test Data 9742, Sex 40448, Drop unused fields 1177, GarageYrBlt does not have any incongruencies 6804, There is 77 of missing data in the cabin column it s usually way too much for this column to be exploitable but as we have a small amount of data we still try to use it in feature engineering 11883, Visualizations 5317, Collinearity 12599, Random forest without hyper parameter optimisation 17367, We drop the irrelevant attributes for our task here 18725, There are 12500 files 10077, With tf 7817, Modeling 8787, The best score 26093, Generate output 43366, Countplot for Labels 18835, Calculating the Eigenvectors 27404, Training the Model 13861, Fare categorize it into 4 ranges 41396, FLAG OWN CAR and FLAG OWN REALTY 18534, The avarage saleprice for which a house was sold was 180 3271, Updating Basement features 14614, Station 1 Recap gradient magnitudes 42800, Encoding categorical data to numerical 8001, Train Lasso Regression 11207, use different function to calculate ensembles 7939, Averaging models 29097, Score df for visualizations 16350, Feature selection 41017, We are not done yet as there are some family groups with all members in the test data 21810, Ownwer occupied properties typically sell at random looking prices 41327, As mentioned at the beginning of this kernel we first compress the data using Truncated Singular Value Decomposition 23272, Cabin 14524, Observations 28014, Because of high dimentional problems we have encountered with by default it returns the matrix in sparse mode 15372, Building Machine Learning Model 3984, Drop columns with a lot of NaNs more than 75 8144, These are the categorical features in the data that have missing values in them 35216, Jaccard Score is more about how exactly the predicted words match against actual words in a sentence 12567, that our training data is cleaned we do the same thing for the test data 24031, Overall sales trend looks to be declining so I calculate relative year size to adjust sales count 28860, Create Sequance 41965, Shops 1775, Train Test Split or Cross Validation 31078, LotFrontage 28678, CentralAir 3992, R 2 1946, Relationship with YearBuilt 41313, Imputation 40245, We can start by looking at features highly correlated with Sale Price 708, Some dummy variables exist in train but not test create them in the test set and set to zero 38150, DRIFT REMOVAL 16974, Correlation between our features 738, Fill with medians 40876, Here we go Its kernel ridge that scores best on leaderboard after optimization followed by ridge and svm The xgb scores worst among the models 32822, Best parameters are searched by GridSearchCV on my Laptop 41465, Since the vast majority of passengers embarked in S 3 we assign the missing values in Embarked to S 33027, Evaluation 18687, The training set contains 20000 images and the validation set contains 5000 images 31222, Data loader 23504, Design CNN architecture 34337, Analyse Target Variable 10644, we can treat Age as Categorical feature 106, Now we have to understand that those two means are not the population mean bar mu The population mean is a statistical term statistician uses to indicate the actual average of the entire group The group can be any gathering of multiple numbers such as animal human plants money stocks For example To find the age population mean of Bulgaria we have to account for every single person s age and take their age Which is almost impossible and if we were to go that route there is no point of doing statistics in the first place Therefore we approach this problem using sample sets The idea of using sample set is that if we take multiple samples of the same population and take the mean of them and put them in a distribution eventually the distribution start to look more like a normal distribution The more samples we take and the more sample means be added and the closer the normal distribution reach towards population mean This is where Central limit theory comes from We go more in depth of this topic later on 36628, let s checkout our label interest level 19535, Grouping on RDD 17048, Gradient Boosting 42935, Adding more features 35856, MODEL SUMMARY 29092, SW dataframe 25892, The Coleman Liau Index 12773, Data Visualization 17653, Out of Fold Predictions 20439, application test 40691, NOW LET SEE HOW COUNT VARIES WITH DIFFERENT FEATURES 5981, DecisionTree Classifier 36738, Building the best final model 675, We want to make sure that our classifiers are not overfitting random data features 41864, Bag of Words statistics 34736, First 3 dimensions of the Latent Semantic Space 34930, Get target 3226, Trendline with error based on pandas dataframe 40063, More Visualization on Numerical variables 33306, Learning Curves of the Models 27409, forward propagation 19815, Hashing Encoder 438, MSZoning The general zoning classification RL is by far the most common value we can fill in missing values with RL 41580, Data Augmentation to prevent Overfitting 633, Alone 7394, All datasets were saved as CSV files and can be easily added to your kernel from the page 6442, Replacing other missing data frame into median for continous variable and mode for categorical variable 6439, Bivariate Analysis 34267, Create the sliding window data set 18583, In addition NVidia provides special accelerated functions for deep learning in a package called CuDNN 1126, The age distribution for survivors and deceased is actually very similar 40330, NLP based stuff 10716, Lets try to understand how survival varies with different features 14497, Reading and Understanding the Data 23651, Discrete wavelet transform 22447, Area chart 28275, Fitting a new model with the found hyperparameter values to the training data and making predictions on the test data 41693, The two dips of time in training set are curious if looking at counts per unit time they might need to be normalised 16558, SibSp and Parch 15683, Hyper Tuned Ensemble Modelling 37016, Treemap of the categories 36701, Evaluate model with evaluate method 5133, Temporal variables 39871, GrLivArea 30113, As well as looking at the overall metrics it s also a good idea to look at examples of each of 14645, Store a copy of the train test data at this stage for workflow purposes 26771, Submit 29335, Basic NN 9887, 1st class passengers are older than 2nd class passengers 15029, Pclass 11195, XGBoost 37997, And here we have information on sell prices for all items in all stores by weeks 1098, Extract Cabin category information from the Cabin number 20479, Credit currency 12511, Fill them nan s 2373, Using custom and existing function in a ColumnTransformer 31249, Fare Age Binning 37491, 1d CNN Convolutional Neural Network 13129, VERDICT WITH BEAR GRYLLS 32889, 2nd level model as a linear regression 22228, Optimizasyon fonksiyonlar n tan mlamak Define the optimizer 18977, Display values in table format with each columns and header with different colors 24769, Fit all models 22538, Data preparation 16623, Hold Out Validation 9262, 3 outliers removed 14404, In order to get rid of outliers and make data more usable 434, GarageYrBlt GarageArea and GarageCars Replacing missing data with 0 Since No garage no cars in such garage 6996, Linear feet of street connected to property 42264, I fill the empty strings either with the most common value or create an unknown category based on what I think makes more sense 9770, The multi collinearity is what we want to avoid when using dummy variables 1892, Defining Features in Training Test Set 3642, Filling in missing values in test set for Fare 3175, on a single core 36645, It s buggy and I don t know why 42818, Augmentations 16636, Datatypes 11492, Check every features 9875, The incomes of passengers boarding from the Q port can be said to be very low 3479, Construct and fit the best elasticnet model 29226, we can prepare our data for modeling 14303, Creating new feature Title 9165, KitchenQual 9291, Imput Missing or Zero values to the Cabin variable span 34613, t SNE 862, Passenger Class Survival rate decreases with Pclass 3654, Creating dummies for all features 12132, Create a corss validation strategy 2 21275, Metric 31533, Numerical Features 31108, Bad Label cols are those columns that the values are not the same between the 2 dataset In this case the training and testing 15996, Searching the best params for SVC 10800, I am not optimistic here as I don t believe that departure place could influence survival rate 2670, Above is the correlation heatmap of all the numerical featuers in the paribas dataset out of this correlation map let s identify the features with higher correlation and exclude those features In the exercise we set a threshold of and exclude all the features with correlation more than 9059, TotRmsAbvGrd 16996, Plot tree 15645, AdaBoost 36334, Display the first 5 images from the training set and display the class name below each image 19940, Pclass 14441, go to top of section eda 7002, First and Second Floors square feet 6828, Correlations 26648, User features 17400, Correlation between columns 19269, CNN on train and validation 9308, Which is significantly worse for OLS and puts us in trouble if we have to explain the model to someone else 41654, General overview 20290, INSIGHTS 25440, Data Augmentation 13084, Decision Tree 39780, Custom Sprinkles 34833, Find correlation with in the features and drop highly correlated feature 42577, The code below repeats it 10 times if you are not confident that this isn t a lucky shot 21835, Manually Encode A Couple of Variables 8855, Create the Explainer 13100, Data needs to be one hot encoded before applying machine learning models 23905, References 12106, Experimenting with ElasticNet 27180, Prediction on test dataset 37003, check the number of orders made by each costumer in the whole dataset 1045, Interesting The outlier in basement and first floor features is the same as the first outlier in ground living area The outlier with index number 3405, Here I add a column of NaNs as placeholders for the Survived variable in the test dataset and then combine it with the train data into a single dataframe data 22006, This is a common problem that you ll encounter with real world data and there are many approaches to fixing this issue 15855, Label encoding non numeric features 18751, We can also use 13216, Performing basic visulization with the help of Seaborn 9280, Conditions to check if data is tidy span 11625, all feature are scaled and ready to use as model input 20324, we re ready for the network itself 15615, Create AgeBands 29917, Hyperparameter Values 12182, Converting the embarked feature 37194, Transforming some numerical variables that are really categorical 22610, Validation strategy is 34 month for the test set 33 month for the validation set and 13 33 months for the train 19449, With a validation score of close to 98 we proceed to use this model to predict for the test set 24850, Only keeping 25 sequences where the number of cases stays at 0 as there were way too many of these samples in our dataset 38125, Name Age Sex Ticket Cabin Embarked All are non integer values rest are integer values 13707, so now let s generate our predictions based on the best estimator model 33859, Splitting into train and test set with 80 20 ratio 40000, Central Tendency 4667, Scatter plots between SalePrice and correlated variables 28159, that we have our model loaded we need to grab the training hyperparameters from within the stored model 42840, China 19288, Train Valid split 40083, Categorical and Macro Features 43145, Check the Dimensions of images 39870, year 7827, Clean test dataset and use the same features we used in training the model 2201, doing the same thing to the fare column 29370, RANDOM FOREST 25466, Cost calculation 15700, Most passengers embarked in Southhampton 73 11134, plot the poly fit data 31257, we build our keras classifier that be used for optimization 43258, Tratamento de datas 16277, Zoom In 24877, let s try to compute the mean age of Names with titles who do not have missing ages 12087, Split train data into two parts 37648, Changing features to sparse matrix 39741, Fare 36857, Exploring the Data 34747, CREATING THE EMBEDDINGS USING KERAS EMBEDDING LAYER 22292, Set the parameters for the network 29104, Convolutional Neural Network 34285, exploring the correlations 7576, Barchart Correlation to SalePrice 41829, Evaluate table of single models 21248, Image data is reshaped to image in 3 dimensions height 28px width 28px channel 1 16929, One third of the passengers are female 12988, Ensembling 811, Numerical and Categorical features 41455, Plot the cross tab 2563, predicting using h2o predict 24342, we extract the features generated from stacking then combine them with original features 801, Finding best tree depth with the help of Cross Validation 6601, Make Predictions 33794, Correlations 12050, XGBoost Regressor 8098, Fare vs Survival 22949, also visualize the count of each prefix in a bar graph 5260, As our next step we are going to train a set of RF models that utilize only the tiny subset of features selected from the model via embedded feature selection algorithm 40073, Label Encode Categorical 7855, Mutual Information Regression Metric for Feature Ranking 39391, Summarise findings 12769, Predictions return probability between 0 and 1 for survived or non survived so i take the argmax of the array to get the max index for each test example 39728, Family name 28388, Write a useful function 33866, Exploratory Data Analysis 13710, CORRELATION ANALYSIS 12415, In here I introduce a new and general method to handle non numerical data with sklearn 32359, Training the CNN Model 12519, categorize the columns on basis of data types 38932, Applying Linear Regression 36258, first vizualize null values on our training set on graph 1950, Dealing with Missing Data 4782, Exploratory Data Analysis 27056, Define a function to get the Mean Absolute Error MAE 4710, Prediction 40012, Target distribution 7280, Embarked 12569, let do the final step of data pre processing normalization Scaling helps in making our data to be in the same range and help prevents domination by single feature with high values 10837, move on toward the predictors 16198, we model using Support Vector Machines which are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis Given a set of training samples each marked as belonging to one or the other of two categories an SVM training algorithm builds a model that assigns new test samples to one category or the other making it a non probabilistic binary linear classifier Reference Wikipedia 468, Clustering 37171, Check the performance of the random forest 2317, Train Test Split how to grade your model 13442, Pclass People of higher socioeconomic class have more chance to survive 22285, Our param grid is set up as a dictionary so that GridSearch can take in and read the parameters 20839, set null values from elapsed field calculations to 0 25593, Rectified Linear Units Most Deep Learning applications right now make use of ReLU instead of Logistic Activation functions for Computer Vision Speech Recognition and Deep Neural Networks etc 34400, Convert Pandas DataFrames to Numpy arrays 24653, img 37789, Network Structure 4031, OverallQual an important variable to look at 10966, Categorical data with few samples per bin 160, EDA of gender submission file 36042, when going through articles about COVID 19 following factors in a country was found as factors that are having an impact on COVID 19 outbreak in a country 27138, We focus now on categorical variables in our dataset 6173, Visualizations 29150, Visualize using Missingness Map 4878, Modeling 5702, GarageType GarageFinish GarageQual and GarageCond Replacing missing data with None 4486, K Nearest Neighbour 12026, check which model contribute how much in ensembling 26665, bureau balance 9964, Lot Area by Price 6899, By creating Age subgroups we can examen the data even more 27445, Events 7904, Second model Logistic Regression 3535, Facet Grid Plot FirePlace QC vs SalePrice 34838, Model Prepration and Training 37106, there are 2 methods 37004, let s explore the items datasets 27006, f1 metric for Keras 13897, Data Visualization 4794, check the distribution of our dependent variable 32927, K Fold cross validation 28987, plot with the features with missing values vs Sales Price to find some insights about the data 25405, CREATING WEIGHTS 19411, So the TL DR of RoBERTa vs BERT are the following 5949, Data Loading and Description 41088, We be using bert base uncased as our base model and add a linear layer with multisample dropout This is based on 8th place solution unintended bias in toxicity classification discussion 100961latest 593873 of Jigsaw Competition unintended bias in toxicity classification overview 2732, Example with decision tree 28374, Wordcloud 15669, KNN 31421, Evaluate the Model 16525, RandomForestClassifier 10046, Logistic Regression model 12742, In this dataset we have some missing values 23954, Applying Linear Regression Algorithm 40399, Submission for GroupKFold 20931, Run the session 42300, For the data augmentation i choosed to 20887, Define batch size 5070, Less good 13574, Ticket is a very sparse data with many values 17806, also add one more feature Class Age calculated as Class x Age 28138, Evaluating our Model 12491, Building a pipeline for lgbm 38754, Logistic Regression 27103, Import Dataset 28009, Test Accuracy 21621, Use a local variable within a query in pandas using 21602, Calculate running count with groups using cumcount 1 9044, There are about 24 features with correlation values 0 12048, Droping a few variables 15529, There are 2 missing values in Embarked which can be found online 6720, Correlation Heat Map 3893, Variance and Standard Deviation 26578, Using our cat classifier 5952, Dealing with Missing values 42542, 3D t SNE embedding 1158, A Random Forest is aF ensemble technique capable of performing both regressioF and classificatioF tasks with the use of multiple decisioF trees and a technique called Bootstrap Aggregation commonly knowF as bagging 39809, reshape the whole x train dataset 27758, The most frequent words 9892, categorize these 17 features 36467, Images from ARVALIS Plant Institute 3 6036, Create regression models and compare the accuracy to our best regressor 23988, we are merging train and test datasets so that we can handle noise and missing data in the dataset 11712, Prepare Train and Test 42894, Sigmoid function 40621, Lets drop the outliers and split into x and y 11662, Gradient Boost 41246, CORRELATION ANALYSIS 42518, Printing the shape of the Datasets 19525, Saving RDD to a Text File 27755, Removing emojis 19043, This is because these images are stored in YBR FULL 422 color space and this is stated in the following tag 14488, now with dealing with age values 42576, Calculating the absolute difference between the possible sums and the score we retrieved and sorting means that we find our labels in the first entry of sorted sum 39257, ANALYSIS BY SENIORITY LEVEL 35583, Similarity Measure 32752, Function to Calculate Missing Values 43331, Ensembleing Predictions 20089, Periodicity 7388, After inspecting all the unmatched passengers from the Kaggle dataset the following manipulations should be performed to correct the mistakes 10690, Missing Value 31950, Train and predict 41311, Drop columns with missing values 19547, Generalization 11559, now use the reduced dataset to train a bunch of models cross validate and evaluate them and pick the best performing model 8693, NUMERIC FEATURES 29579, We calculate the mean and standard deviation of our data so we can normalize it 12776, Encode target labels with value between 0 and 4775, k Nearest Neighbour 14532, We can also drop the columns SibSp and Parch as we have derived a column Family size out of them 28103, Hist Plot 849, GaussianProcessRegressor 9882, Correlation Between Pclass Age Survived 31525, And our ROC curve score is 38587, We have to do samething for the test dataset 4349, The distribution of LotArea feature is highly skewed 24176, Time to actually starting fitting to a model 14818, Fill Missing Age Feature 20399, Loop over folds check performance for each 26355, Cabin and ticket graphs are very messy 21801, Model 2 Remove duplicated and constant columns 38830, Train the model on GPU 23753, Importing a dataset to anlayze the temperature trends 10090, Scaling 22862, Suppose our data lies on a circular manifold in a 2 D structure like in the image below 32340, We had an understanding of important variables from the univariate analysis 17536, Determine board side by checking Ticket number odd 12380, In order to view the detailed plot of any just replace x with the column of choice in the line 2 of box below 42310, Here we implement the log loss function for a given training and validation set 2341, Ensemble Methods for Decision Trees 13840, Target Distribution 35343, Generating a csv file to make predictions onto Kaggle 6576, Embarked 33483, Compute lags and trends 4045, Extract Title from Name 3388, Random Forest 3582, Filling a missing value with simply mode is a risk of Bias 39245, Visualization 33453, Implementation of t SNE 39416, replace the NaN values in Age with the mean value 41947, You can view the animated training video here 39859, Inference 40387, Our next step is to decode the image using our functions defined earlier 32750, previous application 36850, get best score for GridSearchCV 4308, Missing data 39818, so we are almost done but to process the image we need to tell our model that our images are grey scale and we do it by adding a 1 to the shape of our dataset 15253, Submission 14763, Nearly all variables are significant at the 0 36798, Information Extraction 34525, To use interesting values we assign them to the variable and then specify the where primitives in the dfs call 16061, we can say that fare is correlated with Passenger class 29049, Gamma Correction 28343, Analysis based on WEEKDAY APPR PROCESS START 17018, From the graph it is visible that there is some pattern in probability of survival based on ticket type 16980, Support vector machine 19839, Equal Width Discretisation 13537, Gradient Boosting Classifier with HyperOpt tuning 31398, fit our model 7812, Finalize Blender and Predict test dataset 1546, compare the accuracies of each model 1912, LotFrontage 28393, Split the dataset raw features 39110, Embarked 33294, Gender Mapper 6241, See mortality rate percentage for solo females in Pclass 3 is much lower that mortality rates percentage for non solo females 3683, Data Cleansing 3973, Stacking At this point we basically trained and predicted each model so we can combine its predictions into a final predictions variable for submission 37788, One hot encoding Convering the label to one hot encoded 10 categories each for one number 11880, Submission 34253, The following parameters are provided to the net 16115, Logistic Regression 20226, Pclass 6648, Computation of Fare Range 11206, Stacking Averaged models Score 38768, Model Stacking 11933, Submission 2783, EDA 5413, Basically except people have name length between 19 and 20 all survived name length and Pclass have positive correlation with the survival 24381, Imports 10281, XGBoost 5006, Categorical Features 8899, XGBoost 13580, we might have information enough to think about the model structure 26790, Adding the cluster variable 32652, Numerical features with missing values are identified 19430, Check column dtypes 6137, Finaly time for the most important area into entire house 41073, Contractions 19776, Defining the input shape 19662, That only gives us 884 features 14165, Here we deal the the null values in the Cabin column 10908, Check the Feature Importances returned by the Random Forest 12687, Pclass 762, Obtain the Hidden Representation 19264, CNN for Time Series Forecasting 42406, Baseline with Tree Ensemble 24439, Random Forest 8460, Create Degree 3 Polynomials Features 31023, Email font 29168, Exterior1st Fill with most frequent class 14392, I am going to replae Unknown values in AgeGroup feature of both train and tes set 16574, Find out missing data 15478, We create a new feature IsAlone for passengers with FamilySize of 1 4329, Marital status could not be known alone from just their name s 31418, Set Up a Logging Hook set up a logging hook 31784, Selection the best classic model for this dataset 7815, Combine train test data to pre processing 7418, I am not using LabelEncoder here because for most categorical variables their values are not in order 21351, Creating a Training Set Data 1413, FamilySize number of family members people travelling alone have a value of 1 7294, Gaussian NB 4812, information about the dataset 42104, Compiling the model 21215, split to training and validation sets 21568, Interactive plots out of the box in pandas 28017, CLASSIFICATION 40389, Data Augmentation as always is helpful in training Neural Networks 30694, Ordenaremos os retornos dos modelos de maneira decrescente comparando seus ganhos 17708, Here is some basic preprocessing to get fast training and test datasets 33726, EXPLORATORY DATA ANALYSIS 43195, LightGBM Model 30384, Processed test set 2366, Output Submission Matrix for Experimental Stacking 10651, We have reached the point there are no more columns which need preprocessing 17414, Second level learning model via XGBoost 9019, Since there are only 2 rows with null values in the GarageFinish GarageQual GarageCond GarageArea and GarageCars variables in the testing data I ll just set them to the average values since there are so few rows with nulls anyway 19635, Phone brand and model data 43360, Model 5207, let s fit the model and using Random forests ExtraTreesRegressor and support vector regressor ensemble learning using with the lasso regressor as meta regressor 1383, Titanic survivors prediction 29229, Elastic Net is doing much better but still with quite large error rate 2156, First thoughts 37006, Most important Departments by number of products 30196, let s run mice 9523, Evaluation 38629, Define MLP Regression Model and compile it against MAE loss 35899, I hope that this was intuitive enough Now let s compile our model we ll use the popular Adam optimizer a crossentropy loss function and an accuracy metric 17957, MLP Classifier 1943, Relationship with GrLivArea 13057, plot feature importance by various algorithms 35864, Make predictions 15998, We take the best model for predicting 14817, Embarked sex Fare Survived 43016, Gradient Boosted Tree Classifier 28568, BsmtFinType1 32020, Fill NaN values and apply one hot encoding 28028, SAVING THE MODEL 25906, Building text model 35096, promoted content csv 29764, Validation accuracy and loss 10545, Lasso 8654, The Age column contains numbers ranging from to If you look at Kaggle s data page it informs us that Age is fractional if the passenger is less than one The other thing to note here is that there are values in this column fewer than the rows we discovered that the train data set had earlier in this mission which indicates we have some missing values 1406, check the distribution of the cabins in individual passenger classes 2756, Handling the Missing Values 5188, In pattern recognition the k Nearest Neighbors algorithm or k NN for short is a non parametric method used for classification and regression A sample is classified by a majority vote of its neighbors with the sample being assigned to the class most common among its k nearest neighbors k is a positive integer typically small Reference Wikipedia nearest neighbors algorithm 38130, Parent Sibling that survived were 2689, We conclude that 24402, check now the distribution of the mean value per row in the train dataset grouped by value of target 28930, Read Write Locker Help 34455, CA 4 40798, Missing Value Imputation 19595, sub cate id 36916, Survived is the target variable we are trying to predict 0 or 1 21915, Transforming distribution of SalePrice to a normal distribution 14893, Visualization of Features 17906, Filling in null values with means of their respective columns 20857, Sample 18107, As mentioned earlier we use custom thresholds to optimize our score 17028, For all family names in test and train set that are not in overlap family dictionary we keep family survival rate as train dataset mean survival rate and for those families that are in dataset we set family survival rate as the one we have calculated in overlap family dictionary 39983, SalePrice vs OverallQual 41091, I trained for all folds offline and selected the models from best folds to make predictions on test set 42353, Tokenize 31232, Features with max value between 10 20 and min values less than 20 34431, Visualizing the embeddings 279, Fare 23987, Storing SalePrice column seperately as it is the Y label target that our model learn to predict Not to be stored in X or features 38753, The y test is not provided in this dataset 8495, SHAP Values 22529, Replacing object value with an int value 20410, Number of occurrences of each question 7127, Ticket 21165, Reshape 41956, Tokenization is the first step in NLP 15583, 935 is way better than I expected 5985, Submit test predictions 31004, We must specify which data format convention Keras follow using the following line of code 35642, Data glimpse font 20382, Remove unwanted words 24016, Evaluation 22937, analyze the SibSp and Parch data before we move on 3333, As we now have 2 features of Age 1 is orignal with Nan and second with imputated values so droping NaN col 12957, It can be said that missing values of Embarked variable can be filled by Cherbourg C 4096, There are a few recode which Fare is zero 13538, Comparison of 4 models including 3 new models 19064, Specify a test patient 10241, Go to Contents Menu 9204, Everyone who is travelling alone according to the Family feature be set with 1 for Number of Familymembers feature 34767, Pre Trained Model Link 11727, Gradient Boosting Classifier 29099, Only 1 13399, The accuracy score of top performing algorithms in descending order is given below 17955, Gaussian Naive Bayes 28915, Create features 8346, Average Age is 29 years and ticket price is 32 41330, Besides compressing data and making simple algorithms more effective on high dimensional data t SNE can also be used to create intuitive and beautiful visualizations of data 6826, Randomly Missing Data 27416, Tuning hidden layer size 18415, Preprocessed Datasets 13807, Completing a numerical continuous feature 2025, LightGBM 17662, visualize missing data 4359, BsmtFinSF1 Type 1 finished square feet 5920, I now want to drop similar columns by looking at the data But unable to find intersection between columns 10157, Line Plots 14733, Plot Confusion Matrix 42138, let s give the parameters that are going to be used by our NN 30001, Building the Model 41214, Just a notice we can get the same AUC score of by doing sum on the logified data before doing exp on it We should have a clear understanding of what we have done first do a log transformation and a sum in log transform means product in original form 17685, SibSp Parch PLOTS W R T SURVIVED 16215, Reading the data 20912, Load best model 38564, Linear Regression 37913, Defining model 36999, Days of Orders in a week 16891, Cabin vs Survival 20487, Go to top font 42374, Combine 42641, Predicting the ConfirmCases From Fatalities 15866, Validation set 18990, Build a vocabulary of token identifiers and prepare word embedding matrix 5013, Bonus Plots 15286, Creating categories based on Family size 14178, According to the Feature Selection graphs for random forest features of Sex and Title had the greatest influence 39167, How to use a predefined architecture from PyTorch 42328, train test split function 8042, Similarly For testdata we perform same action 20574, First we drop unnecessary columns because they do not contribute to final output 22116, Some features still look suspicious 10886, Correction in the data 26327, Concatenate all features 11256, The initial learners now be specified and trained on the training data 4999, Looks like a good chunk of houses are in North Ames Collect Creek and Old Town with few houses in Bluestem Northpark Villa and Veenker 22060, Quick explanation 29450, Right now our glove dict vector is a dictionary with the keys being the words and the values being the embedding vectors 12768, every thing is okay with the dataset so i predict the output values for submission 13839, Analyze by pivoting features 18898, we use our best model to create output set Please note that here we are working with train X etc 31794, we define 3 shared head layers exactly the same types as used in original kernel 38675, Images Per Patient 23957, Lets Merge Train And Properties To Facilitate EDA 27652, Reshape 21318, Distribution of the target variable logerror 19583, Merge Shops Items Cats features 24122, Avaliando o Modelo 8717, Mean Substitution 11346, Selecting Features for Final Model 30769, Comparing meta learners 16443, Cabin 6390, Shape of Data 2450, 30 First PCA Components explain 75 of variance 9236, Checking out the data 33310, Here we pruned half of the features we have what left there is more important features like 26839, How is ride count based on Month 26645, We have problem here 6898, Some additional statistics for both data sets Average and Standard Deviation 20896, To generate new images the existing images are rotated shifted zoomed and stretched at a certain angle The generator supplies the model with new generated images realtime The steps per epoch parameter in the fit generator defines how often this should happen per epoch The characteristics of the desired random changes for each image are defined here 11423, Find 21925, Submit blended predictions according to various trial and error weightages 21097, Get an idea about the data structure 38579, Loading The Dataset 12053, Stepwise Regression 4780, Model Evaluation 18367, Checking Multicollinearity 26975, Save Model 19840, How does survival correlate with Age 18434, Training of the neural network 19193, Predicting using the Randomforest Regressor 11158, Alternate Method to calculate missing Zoning values neighborhood should be zoned the same most of the time 28962, Numerical variables Types 27265, Calculate conditional probability 26791, A tiny bit better but lets check with more clusters 3181, Random Forest Cpu Sklearn 9816, Sex 21245, The Discriminator model 8740, Prepare Data for Training 14674, Gradient Boosting 34976, Data 38231, Feature Scaling 19302, Data Visualization 30260, Comparing with dumb classifier 37304, Term frequency 32215, Add average item price on to matix df 39979, Our main focus is target variable which is SalePrice 11934, Having a look at the columns 7231, Converting Categorical variables into Numeric 41721, We now create the GAN where we combine the Generator and Discriminator 9818, Pclss Passenger s class 23297, Data Cleaning 20095, Trend and seasonality look similar to the ones which we got by traditional metohod 10172, HeatMap 13407, Classification Report 38636, And our second convolutional layer with 15 filters 34387, Months 12242, Classes 2898, Scaling 9281, Run discriptive statistics of object and numerical datatypes and finally transform datatypes accoringly 43160, Running the model 6651, Categorize Embarked to numeric variable 37709, One Hot encoding of labels span 6446, Label Encoding 40121, First I create an event sequence object converting the state object to event sequence 42779, Dataset looks fairly balanced 3966, Ridge Regression 9192, Cabin 37309, ML Modelling 14881, Categorical Variable 41345, Numerical Features 9834, Building Machine Learning Models 17341, XGB 42761, Pclass 34033, First cross validation of ridge model with alpha 1 and k 10 26419, The analysis reveals some more titles some of which are noble titles or indicate a higher social standing 17345, Random Forest 13491, Age 30872, The fit method returns a history object containing the training parameters the list of epochs it went through and most importantly a dictionary containing the loss and extra metrics it measured at the end of each epoch on the training set and on the validation set 34526, One of the features is MEAN 21615, Named aggregation with multiple columns passing tupples new in pandas 19659, DFS with Default Primitives 11432, YrSold SalePrice KitchenAbvGr BsmtUnfSF BsmtFullBath Id Fireplace HalfBath are 100 hard to say bivaraite normal distribution 13497, Is Alone 9710, Elastic Net 42188, We can check it printing the vector returned by the method 12347, BsmtCond Evaluates the general condition of the basement 23814, explore the latitude and longitude variable to begin with 21035, Insincere Questions Topic Modeling 10824, 0 means males 31220, Plot the feature densities comparing response 1 targets with response 0 24712, Show some image and their model Reconstructions 7919, Transform dataset to only numeric values 39824, we have come a long way CONGRATS we have almost completed our model 34539, Classifying the dataset into duplicate and non duplicate to explore the data 29217, Splitting The Data into Train and Test set 2191, Here OverallQual is highly correlated with target feature of saleprice by 82 26085, I made function to visual output 11478, Alley 13212, prune our tree durating the train fase using gini creterion and max depth 3 23887, take the variables with high correlation values and then do some analysis on them 24858, We can first check whether one of the outputs is globally harder to predict than the other 43123, Random split of traning and test data 26036, load 25 images 31094, BsmtFullBath font 27965, evaluation 34473, Make the submission 31005, There are two csv files that contain the data for the training set and the test set when combined they form the mnist Dataset of 60 000 28x28 grayscale images of the 10 digits along with a test set of 10 000 images 5653, Create Age Group category 2189, Corralation between train attributes 11635, Naive Bayes 41948, Losses and Optimizers 43338, split our dataset in Features and Target 37811, Quick Peak into the data 40922, Predict 31581, Continuous 27047, Visualising Images JPEG 42066, Separate key colums only for machine learning 6197, Submission 3 for SVC without Hyperparameter Optimisation 33480, Fit SIR parameters to real data 6102, Mean price is around 180k USD the most expensive house is for 775k USD and the cheapest is only for 34 9k USD 30418, Define roBERTa base model 11010, Some of the features in the dataset be clinging there for no good 41086, BERT expects three kinds of input input ids segment ids and input mask 26004, PHASE 1 BUSINESS UNDERSTANDING 42544, The distributions for normalized word share have some overlap on the far right hand side meaning there are quite a lot of questions with high word similarity but are both duplicates and non duplicates 10683, NO MISSING RATIO 18424, CV score of the basic average 4424, We can note that there are two outliers that can be really bad for the model as they have very large GrLivArea and low SalePrice 8097, Embarked vs Survival 10560, Handle Missing Data for continuous data 37551, Bonus Part Digit Recognizer 18226, Data Cleaning 13961, Visualization of Survived Target column 36118, Lets plot some columns to find ones with low variance so that we can delete them 7333, Normalized the fare 12606, We can say that Female passangers have higher probability of survival than Male passangers 34533, We now do the same operation applied to the test set 36226, import our dataset We read images one by one from the train dir and store them in the train images array Each image is read in Grayscale and be resized to 50 50 33245, Predicting and creating a submission file 27125, Electrical 6623, Creating Submission 38933, Applying Random Forest 18522, I used the Keras Sequential API where you have just to add one layer at a time starting from the input 30691, Isso ocorre pois a fun o h2o 489, we ll extract titles from the Name feature 33685, Days left in year 28511, The categorical columns have following characteristics 12657, Fitting and Tuning an Algorithm 42346, also tried Random Forest Regressor locally With the number of estimators set to it produced a prediction with MAE of However the running time is incredibly long and boring For Gradient Boosting Regressor the accuracy is with the learning rate setting to 21076, Testing Data Is In The House 28321, identifying the missing values 39687, Remove Non ASCI 21748, Splitting the data into train validation and test set 26077, Data Preparing 9341, Split training data into input X and output Y 30460, Preprocessing with nltk 637, Shared ticket 31911, Importing Python Modules 38958, Augmentations 5014, Here we re looking at sales price by neighborhood and color coding by zoning classificaton of the sale 19464, introspect a few correctly and wrongly classified images to get a better understanding of where the model fails and hopefully take corrective measures to increse its accuracy 7684, We gather all the outliers index positions and drop them from the target dataset 25822, In this competition the train and test set are from different time periods and so let us use the last 1 year as validation set for building our models and rest as model development set 20689, MLP for Multiclass Classification 14440, Distribution Plots for FarePerPerson 12131, Splitting train data into train test 1 10265, Exploring categorical features 36859, get indexes of first 10 occurences for each number 7884, finally I explore new features for example a measure of Age x Class would give better insight of the survival rate 21197, Linear Activation backward 12045, CentalAir variable needs transformation to binary variable 38314, Few Examples after conversion 39272, Format and export data 7606, for categorical features 34226, We don t require as much for the labels as all of them are simply a string of wheat 36363, XGBRegressor Model 2975, Handling Missing Values 33990, Scatterplots for continuous predictors 9793, Imputation with MICE 1521, And don t forget the non numerical features 34609, Extract test features using CNN model 23298, Missing Null Values 890, another decision tree with different parameters for max features max depth and min sample split 9941, Creating the category of the age section 5947, predict the test dataset 2877, if we compare our drop list and feature importance we find that the features life sq and kitch sq are common 31412, From the top 9 losses we can try the following 8309, Neighborhood vs mean SalePrice on houses with three bathrooms 23227, This preprocessing help into getting a better accuracy for the model 481, Remove outliers 10893, this plot gives us some useful information such as children are more likely to survive followed by young and old 42117, The following LightGBM model is based on Peter s 5036, examine our target variable SalePrice and at the same time derive information from many corresponding features 11020, Dropping the Age Band feature 29089, sequence length weights 8706, LINEAR REGRESSION 3386, Logistic Regression Model 29177, Dealing with the Dependent Variable SalePrice 9342, Simple Network using Keras 33041, Gradient Boosted Model 18513, so it looks like my theory that Slice Position refers to the z position of the scan was correct 7461, Dropping Unnecessary columns 32246, the training data and testing data are both labeled datasets 1719, Data Types are Important for EDA 2637, total 335 people have survived 547 people have died in the Titanic 26468, For our simple CNN we use 3 convolutional layers with all having the same filter size of 12706, Embarked 4084, analyse this to understand how to handle the missing data 33303, Cross Validate Models 24131, From unique words data frame we can say that some words are repeated lot and some repeated very less we can only keep words which are occuring 20 times or more to keep the dimension of Bag of world model reasonable 40745, Apply a multiplier 4121, split the data set into train and test which is now ready for preprocssing 32254, Data 25176, WordCloud 29798, Validate dimension of our word vector 24348, Split the train and the validation set for the fitting 17852, check the accuracy for the validation set 4989, Correlation of features 10518, Generate CSV file based on DecisionTree Classifier 34407, Predicting 25280, Prepare Data 27306, Get trian and test data 2369, Logistic Regression 8725, Mean Sale Price by Neighborhood 32058, Forward Feature Selection 3404, Label Encoding 27903, Exploratory Data Analysis 7604, Preprocessing pipeline 42636, Number of tests per day by country 24682, let s define SqueezeExcitation module 21508, Plot random images from the training set 19920, Here is a plot of 10 largest positive and negative coefficients for each gender age group 8508, Discrete Categorical Features Bivariate Analysis 23737, Replacing missing values in Age column with the median of Title group 12974, Name Title 23069, Helper functions 37672, Reading the data 42758, Train the network 15090, LightGBM 28728, Shops and items categorys of the most expensive trades 33885, Bureau loading converting to numeric dropping 16740, sex 37510, Customers in train test and by time 7881, I check in a first model how can age correlate with the chance of survive also related to the passanger Class 40378, Splitting the dataset according to GroupKFold 289, Cabin 27930, Shuffle To make sure the model doesn t pick up anything from the order of the rows in the dataset the top 100 rows of data be shuffled per training step 17651, Adaboost 29105, GRU 25821, Price of the house could also be affected by the availability of other houses at the same time period 127, Accuracy 30886, Keep going 32961, There are some features with correlation values between and We need to remove one feature from such highly correlated feature pairs 34633, We are going to fill the row that wind speed is equal zero 36884, SVM 1699, The plot appears blank wherever there are missing values 23309, Data loading 32755, Monthly Credit Data 16568, Transforming the data 40017, Insights 29784, Create and compile model 1517, Some of the features of our dataset are categorical 9772, Machine Learning 14219, The Chart confirms a person aboarded with more than 2 parents or children more likely survived 29371, ROC curves typically feature true positive rate on the Y axis and false positive rate on the X axis 30947, Predict the label and make submission file 31422, License 20031, Show feature importances 21028, After the installation which takes some time reload this browser page 35472, Visualiza the skin cancer cafe au lait macule 37296, Removing Stopwords 5099, Collinearity means high intercorrelations among independent features 563, submission for ExtraTreesClassifier 8141, These are all the numerical features in our data 34228, And now we can add the rest of the data 38107, you can check if you model is trained well 40663, Calculating Jaccard similarity using NLTK Library 25851, Cleaning Text 31715, Tuning XGBoost 28647, PoolQC 37475, Stop words are words that appear very often in sentences but not necessarily make up the meaning of the sentence 14961, Add family size column 16474, Lets Concatenate both the data frames for Exploratory Data Analysis 13055, Gradient Boosting 19445, As it turns out it does appear to be the case that the optimizer plays a crucial part in the validation score In particular the model which relies on Adam as its optimizer tend to perform better on average Going forward we use Adam as our optimizer of choice 41252, CAN WE USE ALGORITHMS TO PREDICT THE AGE AND DECK OF THE MISSING VALUES 1796, SalePrice vs GarageArea 36659, There are also examples of compound opeartions in morphology 35381, Modeling 14883, Cabin 15128, FamilySize SibSp Parch 17731, Deal with categorical values 38725, The fashion MNIST database is more complex than tha standard digit MNIST database 4816, Fitting the pipeline 32648, Variables starting with numbers are renamed properly for further calling as dataframe elements 17795, Identify families by surname 10664, Clustering 40050, As we are using a hold out dataset to simulate what happens when there is a image group in test that is missed in train we need to selected the proper indices 4334, Cabin 40658, finally we want a single real valued metric for comparing our models and implementations 42040, Grouping with bins 18842, Linear Discriminant Analysis LDA 25299, This does not make ANY sense 38990, Traing for neutral sentiment 4337, First class 2271, Embarked 15279, Survival based on Fare Embarked and Pclass 29865, get pixels hu function 15552, Random Forest 15048, FamilySize 14170, It is reasonable to remove the Cabin column now that we have extracted two new features from it Deck and Room 42762, Sex Correlation 31069, DATA SNEAK PEAK 38422, Train and valid accuracy are roughly at the same low point 27343, item id 39013, Print the death by sex 9251, Predictive Power Score 5583, The next step is deletin column that not be used in our models 6513, Discrete Variables 3369, Initialize the clients and move your data to GCS 21324, Data Pre Processing 19049, For some more EDA we need to convert the categorical features in the dataframe into numeric values 29, AdaBoost 5196, check if any of the columns contains NaNs or Nulls so that we can fill those values if they are insignificant or drop them 18748, We can also 14360, Treemap for the distribution of Survived and Non Survived Passengers with their Classes and Genders 8939, Prepare Submission 16253, Submit 1681, Analysis of a categorical feature Analysisofacategoricalfeature 25721, Prediction 17473, XGBoost 14459, Picking the algorithm with the best Cross Validation Score 1795, SalePrice vs GrLivArea 36387, restart the process with a custom label encoding 3289, First a k fold target encoder class be created for the train set 37942, Basically the picture is the same as for the confirmed cases 5403, make sure there is no more NA value 25838, Cleaning text 16454, From dataset description it was clear that Fare values are not skewed 7340, Logistic Regression 37501, SalePrice strong correlations with GrLivArea GarageArea 1stFlSF TotalBsmtSF 38024, what are sincere topics where the network strongly believe to be insincere 30943, Bedrooms Vs Bathrooms Vs Interest Level 41237, Building vectors 13679, Fare check fare next because it is closely correlated to Pclass 16885, Alone 12008, SVR with rbf kernel 19320, Model Architecture 28457, The remaining two columns are random in nature in terms of their values propertycountylandusecode and propertyzoningdesc 27460, Replace Elongated Words 978, AUC Curve 37010, Best Selling Departments number of Orders 21104, Data preprocessing Identify features This means selecting only needed features and create the proper dataset for the processing 42824, Engine 4438, Predict and save 20049, Some shops have the same name let s check again based on the opening and closing dates of the store and check on the test data 14993, To check correlation 1385, we need to choose a loss function and an optimizer 36678, ngram range tuple min n max n default 1 1 14262, Split data for validation 40435, Scaling Minmax scaler 1040, Feature engineering 28150, Let s plot the network 9174, FullBath 21358, Testing Model on the Test Data 43152, Model Parameters 17434, C 0 best 26180, MS Zoning 9613, Strip Plot 7425, It is clear to notice that the first 100 components contain nearly 100 of the variance 2187, Submit predictions 26529, Get Optimal Threshold 28223, Before we continue with implementing a deep convolutional autoencoder let s free up some memory within our RAM 38848, Attached Car parking is high followed by Separate Garage 14310, preparing the test set for prediction 9089, How do these categories affect price 14755, Exploration of Age 6490, It is worth noting that there are only 7 different pool areas 21340, Implement XGBoot 17810, We start with a simple model with just few predictors 6519, Categorical Variables 14260, Dropping UnNeeded Features 15709, Fare Categories per Passenger Class vs Survived 29154, GarageArea GarageYrBlt GarageCars Fill with 0s 25016, Please note that when you apply this to save the new spacing Due to rounding this may be slightly off from the desired spacing 25640, Prediction 18015, Survived by gender 1154, RegressioF EvaluatioF Metrics 40053, At the moment I m using this as I found it difficult to train without more positive cases 38852, Imputation is done based on Numerical and Catecorical features 73, Missing values in test set 8020, Pclass 20312, A first analysis of the clusters confirm the initial hypothesis that 42375, LSTM 12678, Model 2 20620, Support Vector Machine Classifier 22111, Preparing for sklearn 3569, GarageCars 4 is very strange 6214, K Nearest Neighbors 6782, Name 21514, Text Preprocessing 37529, 1st class passangers are older than 2nd and 2nd class passangers are older than 3th 28507, Submission for predicted model 31721, Train ANATOMY Variable 8129, There is a strong negative correlation between the selling price and Average quality of the material on the exterior Kitchen quality and the height of the basement 1722, The code below is useful to understand if you want to plot a barplot using Seaborn package 39083, Preparing data for Pytorch 15714, Family Members by Gender vs Survived 30132, We need a that points to the dataset 23271, Embarked 36201, html Training and Validation font 11716, K Nearest Neighbors 18257, Model loss graph 3032, All of the models individually achieved scores between 0 42412, Create submission 5049, We are now examining the relation of our target variable to other interesting correlated features and start with several numerical features of size 26666, credit card balance 29911, Distribution of Scores 27391, Tuning min data in leaf 12639, we just have to fill in the missing Age values 16824, Looking at Correlations 39721, given positive and negative words we find top 2 words that are similar to positivie words and opposite to negative words 15348, Split data 12060, Non Ordinal Variables 41125, Standard Deviation of Absolute Logerror 12951, Categorical variable analysis 26251, The Model 10638, Finding Social Standing in the Title 15929, Fare 4702, Cross validate models 29324, probs probs 1 2213, Predictions 19782, Setting the number of epochs 33308, Decision Trees 20527, Data Cleaning 42170, Check the values loaded 34731, Importing dataframes 24506, Creating imbalanced dataset 23940, Item Description Lengths 9227, Neural Network based Model and Prediction 19562, have a look at our config 42678, Improve the performance 15969, As we suposed babies survival rate is high and surprisingly it s almost the same for female and male babies 32708, Creating an Embedding layer 5042, back to sale price 13755, Graph Distribution of Sex Variable 18062, Tranforming the dataset 17666, Here we construct two functions 740, Removing outliers in sample 35387, Visualizing Test Set 16057, you get more confident that person who have age around 30 have more chances to survive 20547, Deep Neural Network with Dropout 26938, Inspect your predictions and actual values from validation data 14107, center Heatmap center 4317, Fare does not conclusively say if ones who paid more were more likely to survive although there are outliers that need to also be considered Maybe looking at a slightly higher level class could help 28018, Multinominal NB 34542, Checking the word length of each sentence in both the columns question and question This help us decide the sequence length during model training 9394, Domain Space 18901, Overall survival states 6026, You can change null ratio on parameters section 13511, Submission 11180, Getting the new train and test sets 24531, sort the values in a descending order to check the age groups which contribute the most to the total number of products 40442, Plotting Accuracy of the traning model 34385, Weekend Weekdays 5200, NOTE 21243, DC GAN with Fashion MNIST 20744, GarageArea column 42555, Prepare submission 42970, Creating Submission File 8948, Fixing Kitchen 40863, Model Building Evaluation 37025, Top 10 brands by number of products 39090, Submission 29215, Concat Categorical and numerical features 11374, Gradient Boosting Regressor 10903, Grid Search for Decision Trees 15983, We can replace the two missing values with the mode that is Southampton and check if there are more missing values 29729, As data is loaded let s create the black box function for LightGBM to find parameters 3305, Missing value filling 8942, Fixing Alley 8681, NUMERIC FEATURES 13366, Missing values 9085, I also wonder if price is affected by whether the row was set to have 1 or 2 conditions 6504, Dataset for House Price Prediction is from below URL 8707, LASSO and tuning with GridSearchCV 43096, Submission 22277, Exploratory Analysis 6621, Gradient Boosting Regressor 8041, Name Ticket Number are not an important feature for prediction 14610, Model comparison 25808, Create Model 28517, 1stFlrSF 20976, Pipeline 31729, Test Dataset Overview 7500, continue analyzing other columns that may be correlated to SalePrice 8802, Lets first impute the missing values in rows 22123, The Stacked model is slightly better when compared to the three base models 34156, Great we have the total sales for each month 16303, Finding relations between features and survival 42733, After looking these 56 plots I found som combinations in which the distribution for repay and not repay is a bit different 37570, Binary Variables 35136, Is there a surge of customers during SchoolHolidays 14160, From the Name feature we can astract other important features such as the family name to identify members of the same family 2977, Merging the training and testing dataset 33238, Write a classifier to predict two classes 32011, In Cabin column the number of null values is very high There are only 204 non null values in 891 rows We can drop Cabin column from both train X and test X 35065, Solution 4 1 Convolutional layers with 16 feature maps 32698, instead of single words let s use bigrams and trigrams as tokens 21899, Execute 24809, We removes columns that mostly 0 for noncategorical variables it is not helpful to our prediction 35874, We have an accuracy of 98 11009, Heat map for correlation between features 42276, Drop features photos column 30341, Here we run iterations using different learning rates 6869, Final View 17893, I believe people with higher number of family members have better odds of Survival 15654, Logistic Regression 28124, Exploring the Keyword Column 16146, Title map 2728, GridSearchCV 41718, Get test set predictions and Create submission 42548, Parallel Coordinates 13246, Y value 21145, Even though the graph is huge this is not really informative 11177, Edit pca 30387, Example predictions on our validation set 30457, Starting with the original training data randomly sample rows to use as the source material for generating new fake tweets 11395, Since Mr 3827, survived column is missing in test df 12501, Tuning max depth and min child weight 2941, Dropping features with more than 50 Missing Values 16155, Modelling 18691, let s run fastai s learning rate finder 38954, Seed Everything 13592, Train Test Split 5088, The pure linear models Ridge Lasso and ElasticNet are highly correlated 23106, Findings PassengersId is an unique identity number positive integer assigned to each passenger 22500, create variable with initializer along with their shape and make your logit with equation 4921, Creating Dummy Columns for Label Encoding the Categorical Features 32986, Random forest regression 23412, Randomly take 20 of data for validation 37791, Start Training 1205, Gridsearching gave me optimal C and gamma for SVR 20398, Models evaluation 28660, LotShape 9420, Using matplotlib Libary 38927, Submissions 41531, what have I learnt Well the height is very similar for each digit 12457, Data conversion 28240, XGBoost 14625, Obviously our loss decreases faster Consequently we may reduce the iteration steps and or increase the learning rate 19382, PoolQC 1453 missing value as most house do not have pool PoolArea associate with null value in PoolQC be 0 not valuable column can consider to be dropped 30290, Forecasting 17381, we know the median age of the passinger classes 28115, Training Function 30464, Creation of a pipeline with prepocessing pipeline 20102, Item count mean by month for 1 lag 10968, The variables that express a I don t have this feature should not treat the 0 as a normal category 15858, Plotting feature correlations with a heat map 815, Missing values in test data 1262, Setup models 4026, Categorical or Numerical 37615, Building the model for training and evaluation 14613, Split into train and validation data 38749, convert this into a visualization for better comprehension 42118, The following Logistic Regression is based on Premvardhan s 4558, BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF BsmtFullBath and BsmtHalfBath missing values are likely zero for having no basement 10440, Univariate analysis 34910, Text preprocess for counts 22952, Reshape data 17467, Name Title 26441, we can evaluate our trained classifier on the training dataset 20238, Embarked 40973, Tip For a String use 39 27626, use hard code to speed up 22386, RandomForestRegression 11804, USING Multiple Models for final prediction 30774, Input as X y for Linear Regression 14121, Bonus Geographic Plots 22698, test 29774, Classification part 15273, Submit 20717, Utilities column 12647, Generating a Submission File 5515, Knn 2570, Report 13144, draw a barplot to visualize this same chart without the stretch in x axis due to varible Name Length 4777, Decision Tree Classifier 41171, Train samples 38516, Distribution of top Bigrams 41831, Confusion matrix for single model 15091, Model Comparison 23204, implement soft voting ensemble in mlxtend 14863, Interesting quite a bit of children in 3rd class and not so many in 1st How about we create a distribution of the ages to get a more precise picture of the who the passengers were 8597, Builing Categorical Pipeline 33242, step to load the encoder previously trained 1528, SibSp Feature 18069, The number of train and test images 8769, Family Size 9334, If we make dummies out of this feature we end up with 25 columns and this can make harder for any model to learn the data The fancy name of this is curse of dimensionality and from its name we don t need much to know that we don t want it 34041, Drop the Resolution Column 1555, First we can obtain useful information about the passenger s title 6710, Explore the Categorical Features 17870, Basic summary statistics about the numerical data 8657, Instructions 39143, Explore Data 28284, TorchText data loader fields preparation 20726, RoofMatl column 14785, Median Fare paid by those who survived is higher than those who did not survive 23992, We are going to transform skewed columns 9069, If the skewness is between and the data are fairly symmetrical 10045, Hyperparameter tuning 478, How good a predictor is GarageArea for SalePrice 30415, Main part load train pred and blend 33991, Correlations 18951, Display time range for labels with gradient 39754, Since I have to use scaled data for the svm and the polynomial features worked best for LR and SVM I ll use the scaled poly set 22959, Augmenting hair with OpenCV 92, Fare and Survived 42794, Inference 13325, Sex converting feature to numerical values div 21533, Categorical Values to Numerical values 33468, The length of competition distances increase with decile classes 12214, Using the pipeline in GridSearch 2891, fill in the missing values 7046, Style of dwelling 13743, Creating the O P file 16743, base models 31744, Crop 26641, From doc id we can derive a lot of content features for the doc which should be features D 29909, Submitting Predictions to Kaggle 29698, lets take them through one of the kernels in first maxpooling layer 23074, Fill Age 35404, Spliting into train and test again 22347, Gaussian Classifier 1162, Statistical transformation 5575, Ridge Regression 28109, Encode the Categorical features 39190, H uma grande quantidade de incidentes cujos endere os associados cont m este termo Block no endere o N s podemos criar mais uma feature categ rica a partir disto 3154, Load the data 20246, we need some helper functions to group our categories 26569, Setting up the neural net 15041, Cabin type is related to the Fare price some cabins like C B E D have a higher price than others 27224, let s dive into the next section where we try to calculate the number of units processed by each station machine and also what s the failure rate for each station 7093, We can simply classify them by Sex Pclass 15794, median is used instead of mean so that the value does not sway too much in a direction 22451, Slope chart 2685, Forward feature selection for Classification 6640, The arrow is pointing towards Base Rate 37311, Under Smapling the imbalanced dataset 37454, We need to use the sentiment as a feature for this encode it using LabelEncode 614, We learn 20968, Compiling our Model 31296, Define Our Transfer Learning Network Model Consisting of 2 Layers 18643, We have 38 special values 3485, The best score in the grid search 7969, Creating new feature extracting from existing 29809, FastText Implementatation using gensim 1034, We isolate the missing values from the rest of the dataset to have a good idea of how to treat them 14305, Creating feature Fare 36221, RandomForestRegressor 16845, Adding features to our data 2161, Assessing model performance 11692, KNN Classifier 35951, First we ll separate categorical and numerical features in our data set 41002, why we need to downsample input 35589, Vertical Shift Range 38503, It ll be better if we could get a relative percentage instead of the count 687, The numeric and categorical features are separated since different prepocessing strategies are applied to them 33353, Timeseries autocorrelation and partial autocorrelation plots monthly sales 39407, Target Value Survived 16859, Family 5104, Observations 10726, Sib Sp and Parch can be combined 27505, Check for Missing Value 38291, Seperating train and test data 41619, Lets start by learning from the past and normalizing the quantity column so we can compare departments to each other on a single graph 4450, Mean square error validation 37941, Fatalities 4305, Inference 18587, As well as looking at the overall metrics it s also a good idea to look at examples of each of 21837, also try transforming our target variable since it s not normally distributed 870, SibSp and Parch 9356, Completing a numerical continuous feature 8310, Neighborhood vs mean SalePrice on houses with larger garage areas 27431, Dummy Creation 19723, For each day 15671, Random Forest 11438, Further Engineering is possible excpecially between Oridnal and Numerical Variables And If you find any meaningful relationship Do it for example TotalBsmtSF BsmtQual 39953, I tried numberers that round alpha 0 1564, One piece of potentially useful informatin is the number of characters in the Ticket column 4054, Getting Family Features 39234, Import raw data 266, Model 19132, And our ensemble is 8271, Create PorchSF Feature 15712, Gender Passenger Class Embarcation point vs Survived 7869, Explore the Parch and SibSp column 27527, Display heatmap of quantitative variables with a numerical variable 3402, Most common causes of outliers on a data set 8612, we have to re transform the predicted sale prices back to their inital state 42965, I dropped it because the Fare column reduces the success values of the algorithms 9585, Matplot Lib 15320, Calculating median values of Age by using Pclass and Embarked to fill up the missing values 11719, Nu Support Vector Classification 15542, FIRST MODEL LOGISTIC REGRESSION KAGGLE SCORE 7422, Check if training data and test data have the same numeric variables 40939, Calling Folds 6145, split the distribution into four bins 0 800 800 1700 1700 2900 2900 max 39295, PRICE FEATURES 30688, Tratar a Base de Treino 16625, Plotting Learning Curve 29802, CBOW Continuous Bag of Words 16382, Checking Feature Importance 24662, New confirmed cases prediction 19409, In its essence a transformer model is a seq2seq self supervised task that only uses self attention Voil easy 10587, Random Forest prediction with select features 10536, Drop first five columns 37635, We use the cross entropy loss and the Adam optimizer 4984, Create a dummy df of the categorical data 42425, Life Square Vs Price Doc 17938, SipSp Parch 4006, Compare with a similar method from the sklearn library 31910, the model is most confident that this image is an T shirt top or class names 18958, Display distribution of a continous variable for different groups 2331, Visualization 16030, we create convert Cabin to numerical feature 4395, Exploratory Data Analysis 3610, Additional ways to look for relationships between variables 2009, Impute Missing Data and Clean Data 21126, First we look at variables with the lowest coefficient of variation then starting with YrSold 43193, Build Simple NN model Pytorch 14455, go to Correlation section corr corr 37998, All info about a single item 2454, Using variance threshold from sklearn 9983, Colomns like PoolQC MiscFeature Alley Fence having missing values more than 80 FireplaceQu LotFrontage also significant amount of missing values 20093, Model Fitting Visualization 8253, Using the IQR Rule 32100, How to get the positions where elements of two arrays match 8580, Feature Engineering 32194, Remove any rows from train where item price is negative these could be refunds 36891, CNN 2 504, Drop the features PassengerId Name Age SibSp Parch FamilySize and Ticket which won t be useful in prediction now 13976, Remove unnecessary columns 28754, Loading the model 2807, missingno 19853, Discretisation using decision trees 22024, seperate that in two plots 12326, GarageCars 15690, Percentage of total survived dead passengers 42045, Divide categories more specifically 8649, Use pandas 16821, There 148 unique values for Cabin this s not important field to be considered 31373, Rotate image 1855, Fit and Optimise Models 34051, let s get our dataset ready for training 35228, How much impact does have 16231, we call our model and give the input and the output column Here the input column be our normalized data and the output column is what we have to predict e Survived 11641, XGBoost 10892, Although this graph is stating that children with age between 0 10 have more survival to non survival ratio this graph is not helping much in getting some useful information 32875, Catboost feature importance 1637, Splitting to train and validation sets 37485, Random Forest 1845, Distribution of SalePrice in Discrete Numeric Features 5738, Ensemble prediction 12838, K Nearest Neighbor 38649, Binning 14205, for XGBoost using CV to better tuning the model 15592, Number of rows and columns 22963, Using it with tf data Dataset API 24428, Converting the labels into Catogerical features 16838, First thing first 4895, Dateset is completely ready now 120, Pre Modeling Tasks 42823, Eval Function 27191, Rebalancing the Data 16524, KNeighbourClassifier 10657, Ensembling 29877, Import libraries 262, Model 26058, We can then plot the incorrectly predicted images along with how confident they were on the actual label and how confident they were at the incorrect label 32393, Machine Learning to Neural Networks 14090, Applying Logistic Regression Model 40009, Gender counts 22972, CAM Extraction Model 7128, Outlier Treatment 32643, Regression 38205, Evaluate 3025, The are no more skewed numerical variables 37977, Confusion Matrix 26660, PLotting the loss during the increment of epochs 38683, Plotting Barplot with number 8579, checking if there is any missing value lest 41561, we have to add our pca data and label to plot them over labels 6786, Embarked 2736, The 22248, use train test split to split our data for validation 18031, Features importance 38894, Backward Propagation with Dropout 31383, Basic Imports 21576, Rearrange columns in a df 39404, Imports 13313, XGBoost 1113, Automagic 40089, Benchmark Models 516, Imports Functions 25786, Embarked 42751, Label encode the categorical features 9757, Title 3288, We now apply K Fold Target Enoding technique on the Titanic competition data by encoding the Embarked feature of the dataset 31082, FEATURE IMPORTANCE 22458, Distributed dot plot 32413, Building model 10862, checking for the outliers and dropping them 1014, Great So now we can reproduce our Intro to local validation from tutorial for beginners part 2Intro to local validation with our new features 11392, check to ensure that the missing values were filled in 11967, Features with more than 50 of none values 12608, Passengers from Pclass 3 have lesser chances of Survival while passengers from Pclass 1 have higher chances of survival 32522, Processing the Predictions 42423, Lets Us Understand Relationship Between Top Contributing Features And Price Doc 12642, Family Size Feature 37329, With the two convolution pooling modules of the current model and the final fully connected module a total of three modules are selected to add dropout 3167, Lasso 39280, TARGET ENCODED FEATURES 29738, the list of all parameters probed and their corresponding target values is available via the property LGB BO 2912, Feature Extraction 36552, Feature engineering 36128, Creating CNN model 33201, Compile and Evaluate Model 14429, Verifying that PassengerIDs 760 and 797 titles were correctly updated to Mrs 8163, SalePrice is skewed and deviates from a typical Gaussian distribution 7982, Merge FullBath and HalfBath 26071, we can plot the weights in the first layer of our model 16051, First Lets check how many person in different Embarked and Passenger Class 36754, Loading the training and testing files 23889, Calculated finished square feet 12795, Conclusions 22462, Population pyramid 8228, Below pair plot is created with respect to the SalePrice with other variables 2242, Combine Attribute Class 25781, Ticket is also like Passengerid which does not matter to Survivel of Passengers 29159, Alley Fill with None 9809, Bar Chart 15955, Assumptions 33347, Total sales and the variation on secondary axis 28588, Kitchen 5927, These are Outliers 35191, Feature Importance 12518, check the columns which have less unique values 4069, Applying Different Machine Learning Models 10835, and feed a list of classifiers to find the best score 19728, Observation 17042, Label Encoder 25807, Dorothy Turner and Dorothy Lopez are rocking it Poor Dorothy Martinez instead should consider moving to another industry 20296, Changing Age Age Band 25510, The Keras Embedding layer requires all individual documents to be of same length Hence we wil pad the shorter documents with 0 for now Therefore now in Keras Embedding layer the input length be equal to the length ie no of words of the document with maximum length or maximum number of words 971, Import whole classification 16673, RANDOM FOREST 32977, Lets scale the data 3955, Create YrBuiltAndRemod Feature 24258, Extracting deck from cabin 19345, Getting test data 3432, Fare values vary greatly depending on the accomodation class encoded by the variable Pclass 33341, Exploratory Data Analysis EDA 3805, ElasticNet hybrid of Lasso and Ridge 31061, Positive look behind succeed if passed non consuming expression does match against the forthcoming input 11490, KitchenQual 33473, Italy Spain UK and Singapore 18138, The two parameters below are worth playing with 26196, Multi label models for Planet dataset 22076, check where our non empty predictions were significantly worse than simply taking text Maybe there is a pattern 15010, that we know that sex is an important factor note that there were more males than females onboard 18520, Split training and valdiation set 14827, Pclass 2413, These are the numerical features in the data that have missing values in them 6034, Converting categorical features to numerical 24, GridSearchCV Kernel Ridge 13729, Handling Pclass 12920, Here 0 stands for not survived and 1 stands for survived 23240, Linear Regression 37466, Necessary Libraries 2890, There is certain features having dtype as int but it should be object or string type convert them 10702, Simple Neural network 28604, OverallCond 21941, Python 9148, I found the standard deviation of the BsmtFinSF2 in houses with basements 1105, Support Vector Machines 11110, List the Numerical features required 43339, If the value is 0 then its black 19719, Data Overview 17753, For roughly 100 parameter configurations it takes about a minute to compute the optimum configuration 5343, Diplay quanitive values of a categorical variable in area funnel shape 5539, Split data 14070, Cabin 10135, Performance 19591, shop and sub cate id 34931, Truncated SVD for logistic regression 27751, Average word length in a tweet 37827, TF IDF 6462, DataframeSelector to select specific attributes from the DataFrame 31828, Random under sampling and over sampling with imbalanced learn 21163, visualizing the number of different labels in traing data 8316, Scaling the numeric columns 4490, OVERVIEW 41707, Back to time what if the accuracy varies as a function of hour of day 9272, No more null values 35640, Submition 18186, Sanity check 36049, Trying with XGBoost Algorithm 24341, first try it out It s a bit slow to run this method since the process is quite compliated 8067, Heating and AC arrangements 2407, Imputing Real NaN Values 1097, Extract titles from passenger names 37727, Missing garbage value treatment 10926, Generating a complete graph with ten nodes 26388, Building Model and Fitting 1033, Before cleaning the data we zoom at the features with missing values those missing values won t be treated equally 36531, we can now look and maybe find the value of distance that is visually identical 36726, Set the hyper parameters for training 5608, Nonparameteric Test w r t Ordinal Variables 28074, we split the training data into the train and validation part 24896, Thing is that I kinda discovered the original outcome of test dataset p 20517, The 25 50 and 75 rows 15037, The Fare for the old is much cheaper compare to the children and young actually 24354, For the data augmentation i choosed to 16645, Decision Tree 20307, We can then execute a Principal Component Analysis to the obtained dataframe 16857, Ticket Class 6567, Cabin value is mapped to new featurewith numerical value we can drop Cabin column for Train set and Test set 8336, Which features are important Let a random forest regressor tell us about it 23518, About url function 32185, Show predictions 7082, first check whether the Age of each Title is reasonable 39159, This is exactly what we want 21022, Extract Keywords or searching for words in a corpus 21480, Build roBERTa Model 33721, Handling Missing Values of Embarked Feature by substituting the frequent occurring value S 23391, the bounding boxes with low confidence need removing 40777, Save Model 32780, Model 41232, apply Convolutional Neural Network CNN techniques on the original data 40135, Generating the submission file 14969, Survival rate of 1st class is higher than 60 irrespective of the gender 20746, PavedDrive column 7332, Create a feature Group size When the size is between 2 and 4 more people are survived 10023, Averaging base models 41792, Examples of misclassied digits 38306, The much needed train test split for cross validation 35430, Firstly we make predictions on each model and then save it into lists this create 5 different prediction lists 1345, We can create another feature called IsAlone 16268, Interaction 33246, Correlation 18202, Same series of charts but for returns 1593, Embarked 5209, i make sure that the traing and the test set cols are in the same order 6002, Cek apakah masih ada missing value 29891, Fit the heteroskedasticity model 14, Models include 12337, LotFrontage 4306, Import libraries packages 18489, Correlation Analysis 11168, Want to add ordinal or int column for Year and Month this is the function to perform that task 23915, pip install pyspellchecker 11918, Dropping Values 19462, Evaluate the model 2561, PARAMETERS DETAILS 32873, Test set 2754, Data Exploration and Analysis 19878, Maximum Absolute Scaling 35821, Stacking didn t work for me 8255, Checking for Missing Values 20641, Above world cloud image gives a good picture of the most common words used in the real disaster tweets 42220, Before initiating the final classification layer a batch normalisation layer be added 32101, How to extract all numbers between a given range from a numpy array 7011, General shape of property 26920, In Case of positive skewness log transformation works 20381, Basic NLP Techniques 35401, Testing Dataset 26802, Submission 24350, I used the Keras Sequential API where you have just to add one layer at a time starting from the input 39743, Cabin 25870, Columns 14747, Best Parameters for Recall Score RBF Kernel C Gamma 23031, Discount Season Presumption 15252, Compare models 7602, SalePrice as target 16317, NOTE 18174, Here is a function for generagting a test dataset 19948, The Name feature contains information on passenger s title 16595, Predict the values on Train data to check Accuracy Score 30607, The control slightly overfits because the training score is higher than the validation score 3524, Correlation Heat Map 11632, Logistic Regression 16131, Diving the dataset into Train Validation and Test 10080, There are some obvious correlations like GarageArea is highly correlated to GarageCars GarageYrBuilt and YearBuilt OverallCond is correlated to yearBuilt etc 22916, Output data for Submission 17539, Training 40290, Everything is ok 42757, Prepare the data 9112, I definitley want to drop the Utilities column 4302, Inference 2174, The same logic applied to Pclass should work for Fare higher fares higher survival rate 42341, See the mean sd and median of response variable 38173, Pycaret 8595, Creating custom transformers 14436, go to top of section eda 17558, Similarly for SibSp column 33994, ANNs are able to better fit data containing non linearities 36740, Predict using our final model 304, train test split 37007, Most important Aisles in each Department by number of Products 19652, Benchmark predict gender age group from device model 10937, After data imputation there no much cahnge in the data distribution so we are using this method to fill in test dataset 9610, Factor Plot 35177, Plot the model s performance 11466, Linear SVM 4275, LotFrontage 26050, We place our model and criterion on to the device by using the 16777, Logistic Regression 29427, Mind taking a sneak peak P 30979, To run these functions for 1000 iterations uncomment the cell below 7728, Combining YearBuilt and YearRemodAdd 15070, Class 15768, Embarked 18554, Age vs class vs gender 20531, Create new features 29705, Submit Predictions 18546, Data types non null values count 34302, Lets Visualize A Malignant Mole 24124, Teste Adicional Com ru do 10100, Check the x 34000, Normal distribution with boxcox 25374, We can get a better sense for one of these examples by visualising the image and looking at the label 14276, Bagging 23422, Number of words in a tweet 42395, Do Sales Differ by Category 16237, The second classification method is GBTClassifier We have to almost repeat the same steps as we did previously and just have to change the name of model and pipeline and call the gbtclassifier and check the accuracy of the model 10597, Sequential model with 3 dense layers 41087, Similarly for test set 11958, Creating all the models with the best hyperparmeters span 8146, Creating Training Evaluating Validating and Testing ML Models 3190, I simply do the following 63, Pytorch Training 37714, Time to test our work 38676, Categorize Number of Images Per Patient 32897, Taking the average of the two word scores 4762, Low range values are similar and not too far from 0 8063, Fireplaces Variable Factor 15559, Filling out the missing ages 39202, Designing Neural Network Architecture 41131, Firt thing first 3880, All our feature transformations steps now live inside pipelines namely scaled numerical pipeline categorical data pipeline score data pipeline and engineered feature pipeline Combining all the features using a FeatureUnion give the data our model train on 10042, One Hot Encoding 14548, Pclass font 12825, Passanger Class Pclass analysis 34720, LGBM parameters estimated using Optuna 39450, checking missing data in application train 8334, Well it is right skewed 463, Scale numeric data by StandardScaler 13373, Pclass 3796, Missing Data 29478, Machine Learning models 19623, Data augmentation 38159, The only required parameters for H2O s AutoML are ytraining frame and max runtime secs which let us train AutoML for x amount of seconds and or max models which would train a maximum number of models 21817, Bedrooms 25958, First we should combine Prior data with Product Dataset for some baseic analysis 21739, As the data consumes high memory we downcast it 27068, again what about test data 6846, Types Of Features 310, Some Regression Visuals to help us understand the current state 17597, Mapping categorical features 21352, From Train Dividing X and Y 272, Model and Forecast 8325, This states that for Neighbours 6 the accuracy is the maximum 22928, With text there is also another handy feature to make The Length 2772, Correlation plot for new dataset formed 1817, You can tell this is not the most efficient way to estimate the price of houses 35889, Make training data 38467, Aggregating by month and calculating fraction of missing values for each feature 24921, Model Building 18484, Since CompetitionDistance is a continuous variable we need to first convert it into a categorical variable with 5 different bins 11518, lasso scores 28881, Decoder 14816, Embarked sex Pclass Survived 12300, Deck 5355, Diplay relationship between 3 variables in lines 13656, Age 20947, Compute the number of labels 30855, Model training 14573, Import Python Libraries 35854, SO 10 classes One hot matrix encoding 5613, Mask Under Min Size 43301, Rodando o Modelo Sem a Coluna Holiday 14911, Cabin 3892, Kurtosis 1926, Fence 12738, Round 4 Voting Classifier 662, Support Vector Machine 10443, We shall check the same for GrLivArea and TotalBsmtSF and apply the same procedure if these are not normally distributed 28744, Black color means that there is weak correlation between the two values 31267, Average smoothing 37079, Prediction Submission 12341, GarageType Garage location 28243, Submission file 21255, ItemNames 7928, For the linear regression models we use both a GridSearchCV for tuning the hyperparameters and compute the best score 34039, Show Random rows 8312, Visualizing the distribution of some features with missing values 16269, the all important surv group of functions 9229, Initialize Neural Network 22689, Attention layer 21161, Shutdown h2o 14656, Age 38939, That is a very interesting plot 3812, Prediction 264, Library and Data 24014, Train 29222, According to the graph we plotted Cat14 15 17 18 19 20 21 22 29 30 32 33 34 35 42 43 45 46 47 48 51 54 55 56 57 58 59 60 61 62 63 64 65 67 68 69 70 74 77 78 85 can be categorized as noisy columns 9978, Numerical variables Types 18321, Create Validation Function 30764, Check the impact of removing uncorrelated features 28337, Analysis based HOUSING TYPE 43288, Um R de 1 18728, let s obtain our test set predictions 16957, Parent children 38764, Random Forest 29788, Denoising Cifar10 Data 6945, Clustering 19422, In what follows I introduce the TweetDataset 31663, center Numerical Imputation 5278, Feature Importance Scores From Model and via RFE 24136, calcualte Model Accuracy Score and F1 score 11222, Find best cutoff and adjustment at low end 17789, Because she is traveling with a friend about the same age and with a Mrs title we might want to set her as well as a Mrs 10845, 1593 18920, Splitting the Training Data 28418, New random forest with only ten most important variables 14731, Predict the label for the testing features 42988, Pair plot of features ctc min cwc min csc min token sort ratio 37784, Visulaizing an Image on the pixel level 12981, Pclass 26556, CNN Model 33044, This section of code gives fit accuracy on the training and test data for each run through 23299, Outliers 35441, To One Hot 24855, I only display what I call temporal inputs as we re simply trying to have a feeling of how well our model is fitting the trends 4855, average all of them 4326, Parch Parents and Children count 32885, Clustering models 13970, Parch Children Parents 22118, Training Base Learners 5851, Submission 42266, Data Visualization 15045, Title 7323, Missing values on Embarked 10168, Regression Plots 1310, Observations 8522, Test Codes 36395, Download datasets 41342, Find 18364, Checking Multi Linear Regression Assumptions 12892, Revisiting Pclass 18142, Save the final prediction 24860, I am using the original training CSV file instead of the enriched dataset as it is up to date with the latest stats from this week 39885, One Hot Encoding 10450, Elastic Net Regression 8757, Check Null Data 14136, Scaling data 30930, How about most correct predictions 3856, categorical feature encoding 39238, Feature engineering cities 13293, ExtraTreesClassifier implements a meta estimator that fits a number of randomized decision trees a k a extra trees on various sub samples of the dataset and uses averaging to improve the predictive accuracy and control over fitting The default values for the parameters controlling the size of the trees e g max depth min samples leaf etc lead to fully grown and unpruned trees which can potentially be very large on some data sets To reduce memory consumption the complexity and size of the trees should be controlled by setting those parameter values Reference sklearn documentation learn org stable modules generated sklearn ensemble ExtraTreesClassifier html 12402, Predicting over test set 39286, RAW FEATURES 33679, Difference Month font 24917, Cases Distribution with Rolling mean and standard deviation 14828, Sex 14629, Great but it be better if we plot this information 43296, Fazer predi es com o dataset inteiro 28394, Setting up models and grid search 16927, Some columns are treated as integer but should be categories 41840, Unigrams 25407, PREDICT 1968, Support Vector Machines 16262, let s take a quick look at the full distribution of scores 2512, Radial Support Vector Machines rbf SVM 35885, Combine data sources 7348, You can fix this with log transformation 34094, Created Date 15428, we construct our models using the following features Sex Pclass Fare Age and TravelledAlone 19316, Evaluation prediction and analysis 13862, Age 12843, Which is the best Model 39014, Parse shipping data to numbers 27050, Histograms 29867, look at an example image 5262, As a final step in our experiments we are going to train a set of RF models with the top 50 features selected by drop column method 134, Using the best parameters from the grid search 19668, The most important feature created by featuretools was the maximum number of days before current application that the client applied for a loan at another institution 2910, Fill the Embarked Feature in the train setwith 0 10131, Linear Discriminant Analysis 15065, Name Title 31410, Unfreeze the model and train a little bit more 2738, This bar chart gives you an idea about how many missing values are there in each column 23181, See We ve successfully managed to reproduce the same score that we achived only after tunning hyperparameters if we predict using these trained models we should have the best test accuracy possible out of those model let s predict using those trained models 6061, KitchenQual 1 missing value in test 26253, Training Function 28680, Electrical 33616, Submission 15918, Combining Friends and Families as Groups 12404, In almost any situation id is an irrelevant feature to prediction 10436, we shall do the following 26214, Augmentation is an important technique to artifically boost your data size 27913, We have missed 3 features GarageYrBlt LotFrontage and MasVnrArea 31321, Reverse the Transformation 27873, Product Life Cycle 13044, Parch 5884, XGBoost 135, K Nearest Neighbor classifier KNN 37088, now we have OOF from base or 0 level models models and we can build level 1 model We have 4 base models level 0 models so we expect to get 5 columns in S train and S test S train be our input feature to train our meta learner and then prediction be made on S test after we train our meta learner And this prediction on S test is actually the prediction for our test set X test Before we train our meta learner we can investigate S train and S test 9962, Conflict with the domain knowledge 29846, categorical data 35088, Confusion Matrix Training Set 32283, Display distribution of a continous variable in group box plot 562, ExtraTreesClassifier 37105, Discretization 22373, Modeling a tensorflow CNN 6647, Computation of Age Bins 5246, Final Check of The Data Before Feature Selection and ML Experiments 39168, inception v3 requires a tensor of size N x 3 x 299 x 299 2329, Predictions and Scoring Classifications 15515, The survival rate does change between different Embarked values 9010, Set Pool Quality to 0 if there is no pool 10253, Go to Contents Menu 8114, Logistic Regression 36495, Create corresponding train folds 26743, First let s look at impact of different types of events and then we look at specific events 16851, Lets take a quick look at all the categorical data present in our dataset 20202, Categorical Features Data correctness 24908, Confirmed COVID 19 cases per day in Spain 13457, Embarked Missing Values 3168, Just take an average of the XGBoost and Lasso predicitons 11193, maybe do a groupby to make this table more manageable and easier to read 1284, Converting String Values into Numeric 4 1 15030, Pclass 1 get the highest Survived ratio 2410, the features with a lot of missing values have been taken care of move on to the features with fewer missing values 18689, A couple of checks 1590, Missing Values 25036, majority of the orders are made during day time 26902, Create Submission File for approach 9 4062, We just selected all the variables with more than 15 of Pearson Coefficient 37055, This column contains string that furth contains titles such as Mr Mrs Master etc 1552, Survived 10700, Encode Train Set 25343, configure the learning process and choose the suitable parameters 19383, Outliers 26856, Final Submission 24838, use Random Forest 40481, Linear Support Vector Machine 10659, Setup the model 1035, We split them to 26631, Split the train data in train and validation 18894, Random Forest parameter tuning and other models 28553, Outliers can be a Data Scientists nightmare 4249, Features 12872, Now let s create a neural network 10920, Creating Edge 32676, Here the six primary regressors and the stacking regressor are fitted to the training set 41477, Plot a histogram of FamilySize 20030, Train with median price over bedrooms 13983, Handling missing values 16092, Age vs Survival 4638, Count of distinct categories in our variable Here we have counted nan values also if any 23082, Few other checks 40171, Prophet also allows to and that s what we do here 9655, These values can offer more as categorical features than numerical data therefore we be converting them to string 3368, we can further join index with orignal dataframe to check where on which features our Naive Bayes and decision tree model is having issue and with further analysis we can tune our model better but this be out of scope for this notebook 8444, Since we don t have other TenC and others Pools don t coincide with any miscellaneous feature we include the pools into the Misc Features and drop Pools columns after used it in the creation of others features 22673, Named Entity Recognition 6432, Selecting specific columns and slicing 27489, Reshape data and define mini batches 28879, Convert to Pytorch Tensors 18462, try applying a keras sequential model this isn t a great model yet just copied over from the source 33173, or 12869, One hot encoding 25309, Creating Data For Submission 16669, Feature Scaling Continuous Variables 8625, The Goal 1209, Ensemble 2 averaging 1545, Testing Different Models 16544, let s encode the Fare column into 5 categories 12410, Neighborhood 9369, Save predictions 34725, Top features description 37457, Make predictions on test 13296, The VotingClassifier with hard voting would classify the sample as class 1 based on the majority class label Reference sklearn documentation learn org stable modules ensemble htmlVoting 20Classifier 9884, Correlation Between Embarked Sex Fare Survived 9249, Standardizing the data 1958, Filling missing Values 4405, Confusion Matrix 6842, Evaluation Blending 10462, Diagonal Correlation Matrix 26627, Prepare the model 3422, Here is the number of passengers in each of the ticket prefix groups 1703, Finding reason for missing data using Dendrogram 40119, Calculating entropy 34246, SEED all 41757, Run the code cell below without changes 12987, Hyperparameter Tuning Grid Search Cross Validation 40042, let s pick the group of 480 rows and 640 columns or the group with 3456 rows and 5184 columns 5827, Visualising DT 41958, Removing links 21180, Getting Rich With Cryptocurrencies 28923, We can create a ModelData object directly from out data frame 11068, Categorical to one hot encoding 6972, XGBoost 16259, Leaderboard 3515, that you have got a general idea about your data set it s also a good idea to take a closer look at the data itself 3676, Pairplot for the most intresting parameters 26803, Save model 7412, Drop columns where there is a large percentage of missing data 36587, Use all training data learning rate LEARNING RATE 40093, Submissions 13834, Analyze by describing data 40450, Neighborhood 6247, And the following correlation matrix can give us guidance on how to fill the numerical columns with missing values 15670, DecisionTree with RandomizedSearch 4129, Handling Missing Values 27352, We should always run model diagnostics to investigate any unusual behavior 38723, we are going to make a discriminator 16755, Fare 12693, For this feature to work the train test index had to be kept as is 42795, build the model now 12419, Process the data with our 6 top chosen features 18419, let s check the xgboost CV score 30861, Load data 22190, Standard split the train test for validation and log the price 31105, Looking at all the diffrent values in each column 10775, MODELLING MODELS PARAMETERS 39495, The function to plot the distribution of the categorical values Horizontaly 2398, Handle new data while using OneHotEncoder 21093, Logistic Regression model 9598, This way we can get the information on all the people you travelled 1st Class on the Titanic 6429, Random Forest Model 27084, We now repeat this process using LDA instead of LSA LDA is instead a generative probabilistic process designed with the specific goal of uncovering latent topic structure in text corpora 21429, Explore Categorical Data 41270, Sequential Colormap 13150, Data Wrangling 38217, Classification Interpretation 15992, Extreme Gradient Boosting 12292, Calculating correlation matrix in python 1081, earlier we split the train set into categorical and numerical features 35859, nd TRaining 43155, Predictions 35329, In this section I have visualized the dataset with just some random images 15349, Train 5811, First seperate train and test 6644, Survived and not survived by Pclass 19070, Data Exploration Analysis 7887, men who were alone have lower chance of survive 31095, BsmtHalfBath font 35382, Try hyperopt 13125, If needed we can use hue too and get comparisions between 4 features 9035, Observations 702, As a starting point let s do a quick heatmap plot 10924, Labeling a node 20842, we ll merge these values onto the df 27772, Generate test predictions 38741, we drop all of the unnecessary rows and columns 4169, Exponential Transformation 35338, Evaluating the Model Performance 4165, Age 25855, Misspelt word typo 26799, Plotting prediction 13033, PassengerID No action required 5941, split the data into Training testing 7302, Observation 24115, SVM 38421, Using sub and validation sets from prevoius steps 38710, Plot Feature Importance 8366, Seperate young and adult people 11627, Create baseline model 40836, Grid search gives best accuracy for max depth 8 min child weight 6 gamma 0 1754, Median imputation Comparing the KDE plot for the age of those who survived before imputation against the KDE plot for the age of those who perished after imputation 18969, Display the contour lines of a 2D numerical array z e interpolated lines of isovalues of z 9150, There is 1 row in the testing dataframe with no value for KitchenQual 24695, check update fn 11388, Looks like there were a lot of very small children on the Titanic and then a decent amount of people in their 20s and 30s 22429, If you are wondering why are there so many ways of doing things The answer is in the matplotlib architecture 20687, Once connected we define a Model object and specify the input and output layers 21392, Building the Predictive Model and Iterate through Country Region wise 28755, Training 22135, OverallQual Rates the overall material and finish of the house 16133, Check Accuracy 33757, STEP Transforming Extracted Data 2416, These are the categorical features in the data that have missing values in them 60, Pytorch Logistic Regression Model 19754, Test predictions 14576, The items present in the train directory 9508, preview top 5 and bottom 5 records from training dataset 13488, EDA 37282, Here is where our first major trick occurred 6200, Multinomial NB 24129, create Corpus from Text coloumn Corpus is a simplified version of our text data that contain clean data To create Corpus we have to perform the following actions 6671, Confusion Matrix 36810, nltk 20576, Filling the missing values column by column using scikit learn 17846, we prepare the submission dataset and export it in the submission file 27104, Dataset Info 43336, store train csv and test csv in their respective format using pandas read csv function 32113, How to extract a particular column from 1D array of tuples 4448, Cross validation 43329, Training 19412, the text that helps extract the sentiment 16846, we dont need the SibSp and Parch variables 3328, Installing the newest release of Seaborn 17986, Within each gender surviving passengers have a higher median fare than those who did not survive 25646, Drop columns with missing values 26434, If there are only cotegorical features with no missing values as it is the case for the current feature selection the features can be transformed by simply using the OneHotEncoder 6489, Most finished garages gave average quality 9687, EDA of continous variables 32241, The tqdm module was introduced to me by one of my viewers it s a really nice pretty way to measure where you are in a process rather than printing things out at intervals 8058, GridSearchCV 15952, Random Forest 11666, Artificial Neural Network 22968, Here is the batch version of the augmentation that flips the image vertically and horizontaly 13625, Submission 26712, Price Data 36061, One Hot Encoding for categorical variables 4963, I create copies of the input data as I modify it at cleaning and feature engineering stages For easy manipulation of train and test sets I create a list with both references to the actual dataframes 29875, Submission 27159, Category 11 Outdoors 21483, Kaggle Submission 43400, Some small experiment only take the sign of the gradient of our attack 21068, Using same technique to our Problem Statement 11493, More Feature Tuning 43393, so out of 16800 samples the model failed to predict around 1600 1007, Better convert them to numeric 3239, Importing the Libraries strong 25257, Data Visualization and EDA 11479, Fence 6428, lets check RMSE as this is used by the kaggle competition for to evaluate model s predictive power 38752, we split our train and test datasets with respect to predictor and response variables 31556, Working with Test Data 42239, Assess correlations amongst attributes 29126, The model2 reaches almost 99 41392, This gives a score on Kaggle of 0 6557, Correlation Back to Top 10341, Based on the previous correlation heatmap LotFrontage is highly correlated with LotArea and Neighborhood 7265, Fare Feature 16880, Feature Engineering 8690, CATEGORICAL FEATURES 12679, Predicting 10386, Visulaizing the relationship between SalePrice and YearBuilt 34831, Find of missing values for each column 7702, let s view each model parameters in detail 26527, TF IDF Word and Character Grams Regular NLP 28167, You can use the below code to figure out the active pipeline components 4176, Top bottom zero coding 38896, Update parameter with Adam 2092, Nothing particularly shocking here 21083, Convert variables into category type 29455, RidgeClassifier 7515, Split the data into training and validation sets 1872, This data looks very messy We re going to have to preprocess it before it s ready to be used in Machine Learning models 18498, Test our RF on the validation set 10573, Checking null values in Pyspark 16630, Submission 10136, Stacking is an ensemble learning technique that uses predictions from multiple models to build a new model This model is used for making predictions on the test set We pick some of the best performing models to be the first layer of the stack while XGB is set at layer 2 to make the final prediction We use a package called vecstack to implement model stacking It s actually very easy to use you can have a look at the documentation for more information 16763, Fare Feature 14267, K Nearest Neighbours KNN 36719, Again picture is more than word 24120, Run the macro model and make predictions 16713, plot the values according to the number fo neighbours 38941, lets try and pick out high low performers 2851, Model and Accuracy 31841, We categorize subtype of shops in 35843, For more about the parameters and details follow this link learn org stable modules generated sklearn ensemble RandomForestRegressor html 16652, Pclass and Age have high correlation so decided to group the data by Title and Pclass and fill the Age column with the median of each group 40454, Total Square Feet 90, Pclass and Survived 10054, Clustering Title variable 17527, let s train 40856, Numerical and Numerical Variable 32788, let s run the binary encoding function 21628, Pandas slicing loc and iloc 6 examples 7065, GridSearch for GBR 18497, The choice of the combinations of my hyperparameters are based on my past experience with Machine learning projects i worked on most of the time the number of estimators should not exceed the 100 trees since the training set is big enough and the computational ressources needed are very big and in this case we re only using a local computer with 16GB of Ram 35949, Ensemble Prediction 18829, StackedRegressor 21682, Nice score with a little effort 1988, Random Forest Classification 31766, The problem with using the sparse matrix is that it is gigantic there are a lot of users and a lot of products 36883, Random Forest 28060, The number of siblings and the number of parents also play some role in their survival 39769, let s use our topic modeling code on this preprocessed DataFrame 24689, Load pretrained weights 37307, Building a Text Classification model 2506, Family Size 0 means that the passeneger is alone Clearly if you are alone or family size 0 then chances for survival is very low For family size 4 the chances decrease too This also looks to be an important feature for the model Lets examine this further 32305, I also want to check a hypothesis that while saving passengers minors were given preference over adults 7100, we add new feature MPPS 23537, We have 112 000 entries and 8 labels and we have 784 columns 11946, We now differentiate the categorical features and numerical features to perform the preprocessing accordingly 31067, IMPORTS 10989, drop these columns 2047, LinearSVC Model 3387, Feature Selection 37748, Technique 4 Random Row Selection 26823, check now the distribution of the standard deviation per row in the train dataset grouped by value of target pre 5979, GridSearch on AdaBoostClassifer 4873, Import libraries for Ensemble modeling 19435, Remove 1 the latter of pairs of 2 highly correlated variables e g remove v2 for v1 v2 pair 24949, Grid search 32802, BernoulliNB 34707, Cumulative shop revenue based on a particular item 20879, Normalization 13699, We ll then take just the first character and assign it to a new column named Deck and take the any numerical sequence right after this letter and assign it to room 220, Model and Accuracy 27880, Time lag in introduction of items 4583, All of these variables are ordinal so we can easily convert them to numerical features later 22946, take a look at the Cabin feature 33258, Submission 24362, After fitting on the training data you can apply the method on the test data 36847, python 26551, We seperate out the Input columns and the Output columns 16849, we move on to the Name variable 42901, Scree plot 33780, Confusion Matrix 11681, Build a Decision Tree Classifier 24308, Confusion matrix 17027, There are 144 overlapping family names in train and test set 3506, Best score from the grid search 9876, We don t have any missing value in Embarked column 40706, GPU test 19343, Sklearn provides a very efficient tool for encoding the levels of a categorical features into numeric values 1114, Deployment 33079, For instance for the Extorior1st feature we got 15 categories for the train set and 13 for the test set 593, Missing values 16868, Prediction 20192, Categorical Frequency Count Plot 30891, find the correlation between the missing values 17337, XGBoost 35491, Making Prediction 23217, set range for hyperparams and library select best choice for these hyperparams in this range 11524, Support Vector Regressor 13835, split training data into numeric and categorical data 29912, Keep in mind that random search ran for more iterations 14278, Boosting 6007, Data Categoric 14282, Handling categorical variables 38453, Replace proper nouns in sentence to related types 41057, I m curious now what the group 1 feature looks in terms of these first two principle components 8804, Replace the BsmtFinType2 based on BsmtFinSF2 by bucketing the BsmtFinSF2 29468, Analysis on few extracted features 34743, Training our own embedding layer in keras 20852, An even better option for picking a validation set is using the exact same length of time period as the test set uses this is implemented here 43254, Campos 667, Extremely Randomised Trees 2184, Fit model for best feature combination 30931, Shortest and longest questions 19774, Early stopping 31052, Tags 607, We learn 33788, Back to Exploratory Data Analysis 31664, Voting Ensemble 32795, The following is the k fold function for XGB to generate OOF predictions this function is very much similar to its sklearn counter part 22844, By default pandas fills the dataframes with NaN 20254, After hyperparameter tuning 24874, Exploring names 10921, Displaying edges 14092, KNN 20560, Prediction and generation of submission file 23897, There are no visible patterns here as well 1060, ElasticNet 16556, we have to head towards the modelling and submision part so let s split the data into it s initial train and test sets 30320, Train Val split 1215, Locating missing data 5660, The basic reason to combine Train and Test data is to get better insights during Feature Engineering 14061, Parch vs survived 2159, Ready 2950, Find out the best parameter values 19299, Data Transformation 37792, Visualize model behaviour 26635, Validation accuracy per class 30285, Active Count 50 41924, Looking at pictures again 42759, Pretty sure that the plot in this notebook not look the same as my local plot 3738, Evaluate the model 16148, some age is missing 30912, Before submitting run a check to make sure your test preds have the right format 36790, Stemming 23238, Conversion of Categorical Variables 22620, Categories per devices 33567, Some more nan filling 19611, Ordinal variables 21131, define our luxurious interaction 9198, Salutation 5842, Lets look at the distribution of all of the features by ploting them 1763, Names 3730, Extract the target variable 12447, For the LotFrontage I m going to analyze the LotArea 27389, Tuning bagging fraction 11238, use XGB to do the train and prediton to compare with RF 31722, Test ANATOMY Variable 43097, Using keras tokenizer to tokenize the text and then do padding the sentences to 30 words 1408, I m gonna get title from each Name in dataset 8456, Box cox transformation of highly skewed features 19150, we can apply these new tags on the actual Training and Test data sets that have been provided 42983, Preprocessing 39350, Convert to submission format 15021, Positive correlation 7753, it s time to delete outliers in our data as they can degrade our model and prediction 3975, Train the model 39402, Categorical Features Exploration 34468, Save the data 40997, Convolution block 38148, Reading and preprocessing 27422, Number of teams by Date 29731, put all of them in BaianOptimization object 20628, Stopwords present in the whole dataset 36498, Basic Data Analysis 25438, Defining the model 26265, Exporting output to csv 1919, Basement Features 6970, Decision Tree 5578, Decision Tree Regressor 18742, Padding and sequencing 23326, Cross Validation 25375, I used the Keras Sequential API where you have just to add one layer at a time starting from the input 21613, Making plots with pandas 20665, FILLING VALUES IN LotFrontage 35460, Visualize the skin cancer at lower extremity 10647, Ticket 11688, Logistic Regression 24905, Confirmed COVID 19 cases per day in US 9516, Family 267, Compiling Model 39030, Choose the best algorithm the most accurate 38522, Sentiment Extraction using Bert 39318, Save model and test set 42263, There was an issue with the unicode character in A Coru a I ll manually fix it 32596, Learning Rate Distribution 38895, Update Parameters 10067, It is very clear that the survival is higher if the family size between 1 and 5 33118, Model 4 26474, We make predictions on images collected from the test data using the architectures we have built thus far Custom CNN sec Feature Extraction sec and Fine Tuning sec 43010, Resampling techniques Undersample majority class 24993, LASSO for categorical variables 13361, Preview test set 20545, Stacking CV Regressor 23231, As the data in y pred is 2 dimensional we convert the same into 1 dim 32539, Data Visualization 20667, WE HAVE HANDLED ALL THE COLUMNS IN THE REQUIRED WAY IN THE TRAIN SET 10809, start from some analysis 30952, Predict with model emsemble 13404, I drop the least important feature Alone from the model rebuild the model and check its effect on accuracy 31925, The learning rate and the batch size are already tuned the only one that remains is the number of epochs 29899, Lets plot 10th label 28952, Preparing the data for output file 8928, Garage Features 29597, we ll finally use the range finder 43251, SVM 31742, LUV Color Space 36093, Load packages and data 27572, ps ind 16 18 bin 38673, Support Vector Classifier 5363, Diplay charts with button options 35848, And convert them to numpy for preprocessing 564, Gradient Boost Decision Tree GBDT 29522, font size 3 style font family Futura Sorting values through frequency of word 32689, The ImageDataGenerator fit method is used for feature normalization 18708, recreate our ImageDataBunch using the cleaned data frame 26657, PLotting the accuracy and loss during the increment of epochs 28717, Mapping our dictionary 35906, First we one hot encode the categories for the purpose of data exploration 1308, Observations 31258, check our initial model 37061, Outliers Detection 4790, Similarly we impute the values for LotFrontage using Neighborhood and LotConfig as indicators 38943, Effect of StoreType Assortment on stores performance 18306, item price features 26311, Train Test Split 21606, Combine the output of an aggregation with the original df using transform 615, After studying the relations between the different features let s fill in a few missing values based on what we learned 29869, The Kaggle competition used the Cohen s quadratically weighted kappa so I have that here to compare 234, Model and Accuracy 25033, there are 206 209 customers in total 21222, we rotated by 10degrees for some images and zooming some images by 10percent shifts heights and widths to make sure we covered all the 9402, look at the dataset concerning only the relationship between GrLivArea the predictor variable and the SalePrice the response variable From now on I refer GrLivArea as just house area for simplicity 26392, back to Table of Contents TOC 19134, ReLU with He Normal initialization 22510, Lets check for first 3 prediction made by model 18361, Histogram 24674, FINE TUNING THE BEST MODEL 14621, To pass this station uncomment and run 20832, We add CompetitionMonthsOpen field limiting the maximum to 2 years to limit number of unique categories 14624, Station 6 Speed up and pass the gate 8026, Similarly For train data 18430, K Nearest Neighbors 15520, The main categories of Ticket are 1 2 3 P S and C so I combine all the others into 4 21668, Inference 9413, Simple imputation font 26199, Utility functions are stored here they are useful and feel free to add these into your arsenal 4952, let s split our data into training data and test data 36586, Use more training data less learning rate 36822, Here we have our tokenizer which removes non letters and stems 34728, Define Sweep Configuration and Run 19295, Training Evaluation 10262, the coding 41747, MLP ReLU ADAM 28409, train with all 24010, Tensors 37070, Optional Standarding Fare 9376, BsmtExposure Refers to walkout or garden level walls 22533, Sex vs Survived 19598, Add more features 2391, Handling missing values 17910, Using dropna False inside value counts method enables us to include counts of null values when performing value counts 12247, Advanced 22917, we inspect the tweets that predicted probability differs the most from target outcome 35516, look closer to some categorical features 7005, Area of the garage 37804, Ridge Regression 8747, Model 27015, How is EfficientNet working 35621, Normalize and split data 25591, LightGBM 2460, SelectKBest 9637, Trying to understand the density value for Sale price 3066, Cabin 33203, Sample of Predictions 5559, Submit Predictions 1764, Creates Title as a new feature 28676, HeatingQC 19665, Visualize Distribution of Correlated Variables 40699, Converting Data into Tensors 43248, We got a very irregular graph and not a satisfied accuracy and the accuracy doesn t increase with the increaing training data 18088, The most green images mostly contain the plants with very small spikes which are just starting to appear 27437, Taking log of both Age and Fare similar to train datasets 891, all data 204, Huber Regressor 41, Deck Where exactly were passenger on the ship 34634, We are going to apply that function 12654, Formatting for submission 24692, We finetune the model on GPU with AMP fp32 fp16 using nvidia apex package 16361, Removing Some Useless Columns 39890, tree 15303 11459, Pclass 21725, O pr ximo modelo Random Forests um dos mais populares 38230, Methodology 11487, Utilities 20623, I have written few functions to perform EDA 39386, Calculate median values for renta grouped by segmento and ind actividad cliente 20762, Done with all columns not lets start with model building 15206, Process cabin number get deck cabin digit number as distance from ship s nose and check is number odd even as side of ship axis 39093, Average letters per word in a question 11519, Elastic Net Regression scores 36146, Train Validation Split 32168, DEFINING THE PROBABILITY OF MAKING A TRANSFER 5089, Skip ahead to the next chapter if you don t want to find new parameters 16746, Feature Correlation 13494, Deck 39277, Samples of seniority 0 are totally new in the catalogue 2494, Chances for Survival by Port Of Embarkation 7744, we need to know how many missing values we have in each field and if they are considerable we should delete this field as we don t have it for many instances and it not be helpful 24891, Support Vector Classifier SVM 12285, For eg 9393, Basic XGBRegressor scoring 20910, Training model 6984, Number of bathrooms 4443, let s concat train data and test data and save a copy of SalePrice and Id 16667, Feature Selecting 20803, Create PorchSF feature 28398, BarPlot 19524, With coalesce we cannot increase the partitions 5019, More Feature Enginnering 20038, The next step is to compute our hidden layer to output weights 2055, We can improve the accuracy of our model by turning the hyperparameters of our Random Forest model 7521, Submitting Predictions 13171, let s save the Survived column from train data set and join our train and test dataset into titanic dataset to deal with missing values and do EDA 23020, check the sales of event day 35752, Other ML Algorithms need StandardScaler such as SVR 32314, Relation between Survival and Passenger Age Adult Status 32811, Level 3 ensemble 20227, Name 1053, we split them to train and test sets 10786, My intuition says that Pclass could have an impact on Survival as higher class may have better access to lifeboats 18565, Looks like they actually traveled alone I correct that data 18350, REMOVING REDUNDENT FEATURES 5300, The simplest way to stack the five base models is to average their predictions This method essentially gives each base model the same wight for each and then combine them Here I use a linear model with L regularization Ridge to find the optimal linear combination of the five base models In doing so the predictions from the five base models are fed to Ridge as five new features 6528, Scaling 41855, Lung segmentation 19306, Evaluation prediction and analysis 41007, we divide the passengers into three categories men women and children 6630, Power of money 18368, Dropping Unwanted Features 16127, Extra Features Title 9863, Histogram Plot 26082, Run new model 39428, Modeling 32131, How to sort a 2D array by a column 7125, Sex vs survived 37897, Predictions 21427, BsmtFinSF2 16082, Survived column is not present in Test data 15381, See the HUGE difference If we had used the average value without considering the Parch we would have gone horribly wrong 32240, Submission 10166, Swarm Plots 23047, so now we Reshape image in 3 dimensions 42551, Run cross validation with a few hyper parameters 4675, SalePrice analysis 5073, Problem fixed Take away Visualizing the data is almost always superhelpful 16986, Ensemble methods 3734, Split the dataset into training and validation 3708, Normalization 3274, Fill these BsmtQual null values with values of BsmtCond and others with None 15567, We need some cleanup as some cabins are numbered F Gxx 38004, We have information about sales of 3049 various items which belong to different categories and departments 5079, Fellow Kaggler massquantity suggests here you need is pca lb 0 11421 top 4Ensemble Methods that we can reduce the collinearity that we have in the data and even might have increased with feature engineering by applying PCA to the whole data set I think it s a cool idea worth persuing 8431, Check if all nulls of masonry veneer types are updated 10154, Example of a youtube video data 31687, Making predictions using the combined network 35855, A conv2d block CONV2D number of filters size of filters ReLU MAXPOOL2D 37787, Data Preparation 8581, Separate the datasets again 11434, Undoubtely Delete the RED Point InterOut 12807, There is no Survived column here which is our target varible we are trying to predict 2820, creating a plot of the most relevant features 23596, Repeating PCA and making another plot of the first two principal components 1398, Fare vs Survived 26884, Create Submission File for approach 3 23306, Select algorithm and hyperparameters 3908, How to deal with missing data 38156, H2o 22294, Training the NN 5663, Extract Titles from Name feature and create a new column 7638, stack1 9987, Zoomed Heat Map 20457, Family status of client 38277, apply our trained model to the test data But before that we have to also convert text of test data to padded data like we did earlier 42459, Categorical variables 6716, Distribution of Continous Numerical Features 34659, There is an entry with negative price 28237, Compile the model 16979, Cross Validation 30534, Exploration of Credit Card Balance 36050, Preprocessing 23484, N Grams and analyzer parameter 41996, Sorting columns w descending order 25447, Testing Datasets 16737, alone 11613, Feature engineering Create new usful feature 39868, Area 1609, Title Is Married 25789, Great We have done all the Features 41489, Random Forest Prepare for Kaggle Submission 3533, ViolinPlot Functional vs SalePrice 33411, Train the model using data augmentation 13663, Class 27188, Semantic Analysis 17475, AdaBoostClassifier 32782, Training 32785, Submission 12601, Random Forest using hyperopt 34669, Cumulative sales volume 18436, Compare Model font div 10570, After training now is the time to recommend top movies which user might like 1056, Ridge regression 17661, Observations 3159, we re ready to set up the data frames 23731, Survival rate is maximum for passengers who boarded at Port C 55 while it is least for passengers who boarded at Port S 35 15635, Survival by Passenger Class and Family Size 42869, Train the production model 17819, We fit the model 5087, The more different the models that we choose for an ensemble the better it ll perform 43002, Understand the sample ratio 20747, WoodDeckSF column 37796, Training a Linear Regression Model 9415, Feature eng Family Column font 4994, High Level Overview 36250, Mimic the IDF function but penalize the words which have fairly high score otherwise and give a strong boost to the words which appear sporadically 30970, Normally in grid search we do not limit the number of evaluations 11284, Transforming target distribution 20121, Modeling with LightGBM using best parameter 15568, That looks better 4597, Kitchen 21173, Visualize CNN Layers 42311, Cross Validation 17522, Predict the output 1173, For the rest we just use a loop to impute None value 4125, Averaged base models score 36148, Define Optimizer and Annealer 8950, Fixing Fireplace 4381, fill missing value in FireplaceQu with NA 30605, Control 7905, Third model XGB Classifier 38993, Helper functions to sort the image files based on the numeric value in each file name 10751, Train Validation split on the training data and Build Ridge Regression 14867, Interesting to note we have a T deck value there which doesn t make sense we can drop it out with the following code 27290, Model with intervention 10215, Take Away Points 10512, Cabin contains a lot of Null values so I m going to drop it 42638, Correlation 14216, The Chart confirms Women more likely survivied than Men 34440, Prediction 24315, As is discussed in other kernels the bottom right two two points with extremely large GrLivArea are likely to be outliers 20266, Correlation between continuous features 33505, Germany 35673, Changing Data Type 37540, Optimizers 18515, We have similar counts for the 10 digits 24659, We need to install pytorch lightning library 34630, We can say that people prefer the morning and evening times for renting bike 15488, Create neural network model 36224, This array contains all predictions 37734, Deeper understanding of subtypes 30395, Making model 29815, CBOW model 8246, Reading Data 31837, Example calculations 31791, Displaying some original test images 13536, Linear Regression 16921, Modelling Training 33338, Quick look at items df 11152, Look for any good correlations to use for imputation 10429, Merging solutions and submission 38279, 97 accuracy That s not bad at all for only 2 dimensions 15329, First we are converting float to string for both the datasets namely test and train 31951, Main part load train pred and blend 24445, WordCloud 14133, Feature Engineering 28484, Number of houses built VS year 14415, Here we are getting 84 accuracy with RandomForest Classifier 11297, Define a method to carry out cross validation 40459, Age 36515, Name Title 7599, Boxplot SalePrice for MSZoning 32054, Random Forest 7239, Model Training 0 21395, We need to create PyTorch native Dataset so that we can train our model 15908, Tickets with the most Predictive power A5 PC 5837, From here lets call our train set as df 18340, EXPLORING QUALITATIVE VARIABLE 11290, Drop columns 37869, Data Modelling 27292, Cumsum signal 2497, Filling Embarked NaN 16717, Random Forests 24551, In case of total products 2 4683, For Basement features we be a little bit smarter 33842, PandasProfiling 5406, About 2 thirds of the people in PClass1 survived and only half of the people in PClass3 survived 39147, Display images 26367, Images the Network gets really wrong 26924, And check if the 35078, The prediction obtained by this solution yielded a score of 0 15555, Support vector classifier 42259, these are all new customer 14384, At First filling all the missing values with 0 41780, We have below an example of 60 digit images from this dataset 43373, Defining the tensorflow graph 9677, Feature fraction Bagging fraction Bagging frequency 41223, Separate out predictor variables e pixel values and label 11779, Outlier Treatment 36373, Submissions 34221, we have 49 images that aren t labelled 5236, Dropping Outliers From Training Set 22372, Separating the Data Set 18055, build up the XGBoost model As I have not taken care of the ordering of the categorical variables I need to make a larger tree ensemble let s start with 1000 trees 22629, We now explore categories 41257, Compile Your Model 6598, Create and fit the decision tree 2412, These are all the numerical features in our data 32638, Tweets 12824, what is Outlier 28406, Create Model 15847, Name Length 42238, Bivariate analysis scatter plots for target versus numerical attributes 458, Fit the training dataset on every model 24941, Q Q plot after StandardScaler 25191, Normalization 16195, SOOO apparently adding the fmaily size increase the accuracy on the training set LOL well theres no crossvalidation so idk 7259, Pclass Feature 27110, 65 is a big number that tells us there are a lot of missing values in train dataset 10841, we are done with most of the Feature Analysis Beging with the Feature Engineering 8497, Advanced Uses of SHAP Values 15824, Standard scalar for test data 42067, Separating the length of the data to use it later 27272, Using normal distribution to estimate each feature 31419, Train the Model 4220, Target Encoding 41120, OverAll Average Absolute Log Error 31068, LOAD DATA 37159, ALL ACTIVATION LAYERS 14943, Final Data 22806, Educational Level Literate without Educational Level 15477, We create a new feature FamilySize that combines SibSp and Parch 36149, Data Augmentation 2442, Categorical data 14801, XGBoost Model 14579, Delete Unwanted Columns 36729, Prediction on Test Set 31609, We were just training our model to predict 8 24239, Exploratory Data Analysis EDA Cleaning and Engineering features 30268, Using different classifier 31666, Random Forest Classifier 7862, Import some libraries for data exploration 14191, LogisticRegression 40849, Imputing Missing Variables 43032, XGBoost model for None 17655, Prediction 3306, dummies 9064, YearRemodAdd 12062, Train Test Split 1740, We first check the number of missing values for fare 32232, We need a goal for our model that we re building 34384, Hour of the Day 29792, Cosine Similarity 34344, Evaluate Each Model and Cross Validate Top Model 31603, Loading the MNIST data 19864, determine the outliers 33729, Install and import necessary libraries 157, EDA with Pandas Profiling 14781, Parch 20306, I want to find a possible clusters among the different customers and substitute single user id with the cluster to which they are assumed to belong 6553, Calculating of Woman and Men survived 6634, Feature Engineering 16358, Plotting Train vs Validation Curve 32954, It says the absolute difference more than 0 523, Disribution of Fare as function of Pclass Sex and Survived 8716, Deletion 19173, Rotated features unused 10645, Fare 694, The precision score for the survived passengers is decent but not good enough for our specific problem that requires precision to be as high as possible 39178, still noise but this time the noise is more intense 3 extra letters instead of one in the left and missing letter in the right 9479, Learning Curve 22401, Yup same people 42358, Remove punctuations special characters numbers 36230, After creating the model we compile it 38098, Modelling 25045, Department wise reorder ratio 20474, Region registered not live region and not work region 30934, Ridge or L2 regression is the most commonly used method of regularization for the problems which do not have a unique solution It adds penalty equivalent to square of the magnitude of coefficients Unlike L1 it don t srink some of the coefficients to zero It srink the coefficients near to zero but it never by zero 38474, Axis 1 As Feature 41914, As well as looking at the overall metrics it s also a good idea to look at examples of each of 22815, item 11373 was sold 2169 times at shop 12 on a single day in October 22409, There was an issue with the unicode character in 37481, SpaCy tokenizes the text and uses it s internal model to create linguistic features 11151, try another method imputation 5938, we have to encode a categorical values 1090, A heat map of correlation may give us a understanding of which variables are important 7061, Same skewness analysis for target variable 39034, Plot in 2D 8652, Use DataFrame 1078, NOTE i simply used median to fill na values actually there is lot to explore when you do feature engineering 7014, Rates the overall material and finish of the house 18722, save our model s weights 12659, Predict the Actual Test Data 9866, To understand the survival rate according to gender 11992, let s predict test values 21091, Split data set 1140, GridSearchCV evaluating using multiple scorers simultaneously 9706, Linear regression 18658, Test Data Images 9957, GBDT 39453, checking missing data in previous application 1565, Another piece of information is the first letter of each ticket which again might be indicative of a certain attribute of the ticketholders or their rooms 9486, Evaluate Model 24541, Total number of products by age group 20174, To verify lets pass the optimal parameters to Classifier and check the score 7433, XGBoost 20481, Duration of credit DAYS CREDIT 6698, Moving to the point plot 5957, Gender and Survived 34963, Dropping Columns that don t heavily Influence the Outcome 16599, Plot the distribution of each feature 8157, Correlation study 33459, Assortment 7765, Lasso Regression 1108, Gaussian Naive Bayes 34694, Lag everything 27975, hist 35183, Projection into 2 Dimensional PCA 713, Looks like a good thing to condition on 3964, Parameters 13716, Replacing the two missing values in Embarked with the most common value under this feature handling missing categorical data 43243, Testing 2384, Two types of Pipelines 9840, Decision Tree 37349, Lets check missing data 19429, Drop TARGET from train csv and combine train test csv 4228, Direct Method 2017, Modeling and Predictions 14936, we need to first study of our data 23270, SibSp Parch Family Size 39881, There are 11 NAs 32784, Prediction with TTA 3013, This is to make sure that SalesPrice values are distributed normaly using function log1p which applies log to all elements of the column which fixes the skeweness of the distribution 41033, Submission to Kaggle 29931, 3D Plots 3244, Creating 2 new variable for each data type 10404, View statistical properties of the data 17552, Check if any remaining null value for age 27635, Several of these columns have 38992, We not use input test in this notebook as we are not intending to submit We split the entire training set into train dev cross validation and test sets later 39948, Lasso regression 26446, The high number to the left around 0 and to the right around 1 suggests that the classifier was quite certain in most of the predictions that turned out to be right 32882, Linear models 17971, Sex 20178, Reducing Dimensions using PCA 29384, ARCHITECTURE span nbsp nbsp 1st convolutional layer span nbsp nbsp no of filters n1 16filter dimentions 5x5 we use the relu function as the activation function for all the layers except the output layer we need to pass the dimentions of the input as input shape only for the first layer 16503, Creating Data Frame of models scores 20449, credit card balance 28590, There is a clear positive correlation with the SalePrice and the quality of the kitchen 22019, Explore the interaction between var15 age and var38 43131, Confirmed Cases 3 Best Predicted 20674, How to Confirm TensorFlow Is Installed 22778, Firstly creating Date column and dropping the unwanted column and reformatting the date column 7107, Gradient Boosting Decision Tree 1730, Plot Pclass against Survived 6083, Data Cleaning 34249, Plot The distribution 1603, Categorical Features 42030, qcut to change continous values to ordinal groups based on quantiles Try to be the same number per range 21103, Data Cleaning 2953, Convert into Dataframe for final submission 41365, Sale Price FV RL RH RM C 32251, Alright so we made a couple mistakes but not too bad actually 5678, Print the average age based on their Title before filling the missing values 15466, Feature Pclass 32422, MNIST Classification using CNNs 41511, Logistic Regression 1363, Model evaluation 31755, Selected text is a subset of text 35682, Ensemble Algorithms sec 24837, use the decision tree 20225, Survived 32085, Figure 5 Distribution of the absolute correlation between log and our variables 36089, And individually 26366, The Journey of an Image Through the Network 36890, CNN 1 15236, Load data 7658, evaluate base models 38104, Building the Convolutional Neural Network 33281, Building the Feature Engineering Machine 5548, Check Data 28267, Most number of categories are present within Neighbourhood followed by Exterior2nd and Exterior 1st 35561, approximately 90 of the data 1294, We need to do some preprocessing to this test data set before we can feed that to the trained model 37533, Importing Common Libraries 12480, AgeGroup 39994, Blending of models 16900, New Feature Child 19374, Feature Selection Engineering 14398, Feature Embraked 5500, Imputing the missing values 16159, Ramdom Forest 25656, Here are a few medium sized connected components 25894, Dale Chall Readability Score 32702, Cleaning data 22592, Several shops are duplicates of each other 41208, len prediction len GT 40157, No if there s no Promo2 then there s no information about it 18973, Display the distribution of a multiple continous variable 28292, Intensive Ignite usage part 36077, Utils 13420, Submission 6840, Cost Function Cross Validation 18847, Grab a statistic summary of the training set 32675, Each of the six elected regression models is hereby submitted to scoring based on the Root Mean Squared Error metric 10572, For visualization before using visual library matplotlib seaborn we need to convert SparkDataframe to PandasDataFrame 12330, Missing Values 15453, Cabin 3419, grab the wives maiden last names too 1562, Parch 15269, Logistic Regression Algorithm 15880, To bag or not to bag 9989, Features engineering 8726, Sale Price and Living Area 31376, Scale image 18390, Create and submit the submission file 23413, Normalize data by subtracting the mean image 18158, Setting up models and grid search 14529, Sex 4427, check the numerical variables once again 27298, Global Forecast 4637, Look at the different values of distinct categories in our variable This method list down any missing values nan as well 28146, The pattern defined in the function tries to find the ROOT word or the main verb in the sentence 40451, NeighborhoodLotArea 35559, Parameters 10728, MODELS 31087, MasVnrArea font 22050, Checking for missing values 30967, Grid Search Implementation 29633, From our top 3 best perform model I ll try to combine them into soft voting classifier improve overall predicting performance 22012, One hot encoding 32354, Making model 18397, Run the next code cell without changes 14995, The survived passenger was not much 2380, Use of stratify when performing classification problems 5240, Binning the Rare Category Values 14188, Creating Features and Labels 33485, Add country details 9705, Onehot encoding categories 27063, Tweets Locations 8107, Embarked 18956, Display distribution of a continous variable 39288, TARGET ENCODED FEATURES 1692, Describing Categorical and Numerical features separately DescribingCatAndNum 14807, Missing Value 41265, If you re a front end developer it may be easier to understand 13903, Train Test data Split 20952, View model summary 6565, Missing Data 6388, At first we have to prepare the data prepare vectors for female and male passengers 10344, Adding Features 28494, Create a model 2926, AdaBoost 39126, EXP 24516, Data cleaning 3742, EDA 14522, Family Size 16987, Boosting 31915, Normalizing Data 1964, Pairplots 32151, How to subtract a 1d array from a 2d array where each item of 1d array subtracts from respective row 12934, Embarked Column 6505, Explore Data 30081, I ve given a number to classify 15050, IsAlone 32759, Model 24978, lets train the model 14734, Compute Metrics from this Confusion Matrix 14375, Analyzing Feature Age 25462, Applying the model to your validation set 26895, Using Pipelines 10796, Naturally after Parch category let s check SibSp 42790, Observations 24433, Deal with Categorical features OneHotEncoding 8321, Creating output CSV file in the required format 22855, Add Holiday feature 21231, Define the learning rate 11306, Supress Warnings 1330, Correlating categorical features 4380, check missing values in these two feature 36526, Hyperparameter Tuning Grid Search Cross Validation 25588, Linear regression 30416, Define hyperparameters and load data 23303, Drop our target feature from training data SalePrice 13379, Imputation of missing values in Cabin 32955, We should steadily add correct samples to our submission286 in order to get 0 31884, labeling of column which contain catagorical data in object data type Sex and Embarked 39805, start this notebook with importing all the necessary libraries we would need and the dataset 3245, Correlation matric for numerical data 38637, We have a dropout layer which its dropping rate is set to 25 of inputs 7995, Analys Labels 28025, MOST SIMILAR WORDS TO 41022, Here is a summary of which passengers are predicted to live or die from the previous prediction rules 6108, LotFrontage 6111, Nope 6423, Feature Engineering on Test Data Set 22920, look at our first feature Pclass 35206, Common Observation from all Residual Plots The main purpose of viewing residuals plot is to analyze the variance of the error of the regressor Here the points are not randomly dispersed around the horizontal axis This gives an indication that a linear regression model is not appropriate for the data A non linear model perhaps tree based model is more appropriate 34173, Well 20433, Loading data 1176, also take a closer look at GarageYrBlt 5835, Spliting data back into train df and test df 9505, Know how to import your data 28282, Set up data store 31415, Or you can take advantage of the fastai TTA 6283, We have now finished feature engineering I tried many different methods of this including creating polynomials and also interaction variables 7209, The dataset contains 81 columns and the SalePrice column tells us the price at which the house was sold 20783, Viewing Columns 14087, Training Data 13275, Features engineering FE 24822, Encoding Dataset 10565, Visualize Predicted vs Actual Sales Price 19065, Lets look at a prediction for the test patient 19259, We use the current timestep and the last 29 to forecast 90 days ahead 38155, Running predictions 4090, now we are dealing with the big boss 30975, We can also evaluate the best random search model on the test data 28872, RMSE 5891, Test data 25886, Histogram plots of number of chars in train and test sets 10153, We aren t able to add legend so let s use different technique 37441, Couldn t find any evidence or relation between them in positive tweets 14984, Converting categorical variable Sex Embarked Title to numeric values for both the data sets 30456, Example usage 10656, Interpretation 27427, People were travelling alone or with 1 sibling spouse at max 8225, Heat Map for the data 12481, Family Size 15190, Replacing Ages 1617, StratifiedKFold is used for stratifying the target variable 36096, Feature engineering There can always be done some feature engineering comment challenge lstm blob master toxic comment 9872 model ipynb even with text data 42956, The number of people with two or more siblings or spouses in the data is very low 29069, Numeric categorical interactions 23376, I ll also add a wrapper function to call this function multiple times for multiple images 27894, We be using the Sequential model from Keras to form the Neural Network Sequential Model is used to construct simple models with linear stack of layers 17537, Create FamilySize bands based on SibSp and Parch properties 12555, Random Forest 34394, Crime Coordinates 41858, Our target variable is binary and not well balanced but for now for simplicity I leave it as it is 35844, Prediction 17698, SUPPORT VECTOR CLASSIFIER 31642, Address 40068, Correlation Study Numerical Data 2832, Library and Data 29476, Merging all the extacted features 10192, Creating columns from each categorical feature value 19096, Data cleaning 14551, Children less than 5 years old were saved in large numbers font 22527, Pandas Profiling 22037, impute the missing values with some value which is outside the range of values of the column say 99 18286, Pipeline Creation 25746, the shape of the foreground is enough 94 of validation data are assigned the correct original size after 1 epoch 18270, GENERATING WORD CLOUD OF DUPLICATES AND NON DUPLICATE QUESTION PAIRS WE CAN OBSERVE MOST FREQUENT OCCURING WORDS 10528, Feature Generation 6559, Explore Age distribution with kde plot 20253, We can look at the feature importances 32627, Building a Text Classification model 4661, Find out the Relationship with categorical feature 22156, Data read in and prep 846, DecisionTreeRegressor 8765, Graph of survive and unsurvive is almost similar 18493, Test Set Adaptation 20846, Create features 28112, Predicting the Test Set 32197, Only keep shop category if there are 5 or more shops of that category the rest are grouped as other 4107, Basically We don t know test dataset s information so we have to use train dataset s info 5545, Submit Hyper Tuned Baseline Model 21757, For missing values in ind nuevo we can fill in missing values by looking how many months of history these customers have 19860, finally here are the outliers 24382, Exploring the Data 18684, There are 25000 files 21997, 42000 images splited into 189000 images 12686, Survived 14248, Children less than 5 years old were saved in large numbers 14142, Random Forest 16741, double check 12921, females have higher probability of survival than males 41412, Ther are so many features and so many possible angles from which we can analyze them 3459, We can estimate the performance of the model out of sample using cross validation 34537, Defining the model 24527, Number of products by activity index and sex 31840, Shops dataset preprocessing 2245, RandomForestRegressor 1368, Title grouped 12978, Family size 16292, Importing Data for train and test csv files 850, Comparison plot RMSE of all models 41482, Convert the DataFrame to a numpy array 14143, Bagging Classifier 123, font color 5831bc face Comic Sans MS Before Scaling 41563, PCA in 3 D 42050, Change floats to integer to save resources 12041, I m gonna write two functions to help me in imputing missing values in variables 25039, No re ordered products 8608, Perform label encoding to all ordinal variables 18681, extract the files in train 6312, Extra Trees 13132, VERDICT WITH BEAR GRYLLS 17342, Gaussian Process 1327, Analyze by pivoting features 36619, PCA Dimension Reduction 30643, Machine Learning Estimation and model evaluation 21728, Standard Prices and Outliers 36057, Feature Season 32108, How to print only 3 decimal places in python numpy array 23563, 720 1280 3 is the most common image size available in the train data and 10 different sizes are available 32650, Numerical and categorical features are identified and segregated into two speficic lists 12358, Masonry veneer type and masonry veneer area 16785, Neural Network 38278, Submitting our predictions 7191, Normalization 8598, Getting everything together using ColumnTransformer 3625, Another way to visualize housing prices by month and year 5524, Estimate missing Fare Data 6285, Hence in order for the output files later to be accepted by Kaggle I convert this to an integer 36535, It s often a good idea to pre process the images before doing anything with them 558, Decision Tree 4789, We impute the missing values for zone using neighborhood as an indicator 39056, Prediction 34444, In this case what the melt function is doing is that it is converting the sales dataframe which is in wide format to a long format 26364, Confusion Matrix 23525, Majority voting 6991, Remodel date 7885, Less values give a clue of more survival mean however a crosstab maybe would give more clear information 37225, Choose Embedding 13476, The score for the new training sample is very close to the original performance which is good 28077, Prediction 40100, Model 42932, Merging test and train 40927, Melanoma Samples Generator 4162, Weight of evidence 24553, In case of total products 1243, The Goal 7680, Outliers visualization 8488, SGD Regressor 8923, PoolQC 22714, Creating the image matrix for the dataset 17658, Observations 31311, Store 1 Analysis 673, With these optimised parameters let s have a look at the feature importance that this classifier gives us 28086, Cross validated stratified and shuffled 38555, just build a model using the mean num of rooms for the missing values 32154, How to compute the moving average of a numpy array 12461, Feature Transformations 15056, Model 13475, References 5148, Temporal variables 32270, Relationship between variables with respective to date multiple series 19618, Blending 5946, enode a categorical data 37820, Remove twitter handles 7625, num iterations default 100 alias num iteration num tree num trees num round num rounds 80, All the cabin names start with an English alphabet following by multiple digits 35804, distribution of residual errors looks normal except for ET and SVR 3686, Feature Engineering 35840, To learn more about sklearn DT parameters what they stand for and how influence the model follow this link learn org stable modules generated sklearn tree DecisionTreeRegressor html 41013, we found 80 woman child groups for a total of 230 passengers 171 women and 59 boys 18629, Applying Decision Tree 4641, Percentage of Missing Values in that variable 33790, That doesn t look right The maximum value is about 1000 years 21125, In the previous part we used DataRaw 4908, have an Eagle s Eye View 5700, Imputing missing values 28477, New features based on TAX 23052, its time to evaluate our model 2788, PyCaret s classification module is a supervised machine learning module which is used for classifying the elements into a binary group based on various techniques and algorithms 4797, Feature Engineering 40160, Clearly stores of type A 40430, Plotting the base learner contributions to the ensemble 23614, Bar plot of missing features 2096, Categorical features 30909, For this column propertyzoningdesc the information behind the code could refer to 16513, Random Forest Classifier 42841, South Korea 19959, It could mean that tickets sharing the same prefixes could be booked for cabins placed together 33143, we round the predictions set them to integer update the submission dataframe and save it as CS Oof 2022, Since our lasso model performed the best we ll use it as a meta model 1038, Number of missing values per column 5472, More rooms more Squarefoot More Garage Area Garage Cars Total Basement SQFT 1st Flr SQFT 29631, Support Vector Classifier with RBF kernel 24156, modify the annotation file for feasible use 36104, Attention layer 31374, Flip Image 43363, Feature importances 9632, Importing important libraries 16863, Fit the models 13977, Map Sex and Embarked to numerical values 37493, Submission 12815, Data Exploration Visulization 35497, Implementing with Keras 37365, Fare Vs Survived 31835, Function to get proper weights and scales 15658, Random Forest 16464, Ticket 41321, Loading in files 18523, Set the optimizer and annealer 5021, Dummy Variables 24248, The best categories for age are 42868, Find the best epoch 16254, Scripts 4823, Standard approach missing data scaling imputating etc 1633, Standardizing numeric data 17533, Fill EmbarkedCode with the most frequent value and transform it to numerical categorical feature 7896, same for the FamiliSize columns 29902, Lets create a simple model from Keras Sequential layer 34224, get y needs to return the coordinates then the label 29999, Plotting the scores of all models 15212, Model Keras MLP 10591, Gradient Boosting 12262, Categorical variables 23325, Item name Tfidf text feature 3008, strong Visualizing the data strong font div 24790, Metric 27021, Confusion Matrix 1906, OverallQual GrLivArea GarageCars GarageArea TotalBsmtSF stFlrSF FullBath TotRmsAbvGrd YearBuilt YearRemodAdd have more than correlation with SalePrice 12065, Correlation Analysis 29808, Glove pretrained WordVector of a word 40618, And a simple neural net 10221, check correlation cofficent between our features 32940, Create the vocabulary the word count and prepare the train X 153, Submit test predictions 894, SVM Classifier 11894, Outliers Handling 42608, Optimizer Settings 4474, Encoding Titles 23401, save the weights so that the model can be used in an inference notebook 12749, extract some grab the title out by using the quick and dirty split method and then add a column called Title 11488, Functional 4130, Using Imputer instead of fillna 17340, XGB 10257, Get rid of NaNs 36411, Statistical Distribution 26213, In object detection with bounding boxes it is always a good idea to randomly plot some images with their bounding boxes to check for any awry bounding box coordinates 13615, separate our features into ordinal categorical features and nominal categorical features then we 28951, Predicting the Survived label on the test set 7037, The general zoning classification 7585, Scatterplot SalePrice vs all SF 3527, Scatter plots between the most correlated variables 38790, Save 29838, Helper functions to the main association rules function 21177, Displaying output of layer 8 2167, Our hypothesis is that the higher the class the higher the chances of survival 21396, Create the model loss and optimizer 17559, Similarly for Parch column 9597, There were three classes on the Ship 1 2 3 32371, Loading Image Meta Features 2696, First of all let s start by replacing the missing values in both the training and the test set we be combining both datasets into one dataset 11499, if you need scaling you can also use minmax scaler 26346, Clean and Edit Dataframes 23921, Model deep learning seq 4131, Some Plots to Visualize the effects of features on driving the Sale Price 28368, location 8032, There are outliers for this variable hence Median is prefered over mean 32076, Here 15589, Your submission scored 0 24139, Decision Tree Model 21821, Features 19601, prediction xgboost 28031, GLOVE INITIATIONS 39239, Analysis of opening periods 11696, Single Imputation for categoric columns 36634, To compare performance and efficacy of each technique we use a K fold cross validation This technique randomly splits the training dataset in k subsets While one subset is kept to test the model the remaining k 1 sets are used to train the data 1068, Fit the model on test data 2475, now our datasets look like this 35546, perform some feature selection like Lasso 2518, Gaussian Naive Bayes 19608, Bivariate analysis 4548, Finding Missing Values 23898, Hurray 40869, Optimize Ridge 30316, Start positions and end positions of selected texts in tokenized source texts 34261, Rolling windows 3304, Special example missing value filling 32598, Evolution of Search 8873, GarageCars and GarageArea are highly correlated to each other and from the heatmap both are highly correlated to the SalePrice Hence removing GarageArea from our analysis since it adds redundancy 24406, The values towards the top are the most important features and those towards the bottom matter least 4364, One Outlier may be detected with TotalBsmtSF 6000 and SalesPrice is low 7423, I simply choose skewness as the criterion to select between standardization and Yeo Johnson transformation because I am not training my data on linear regression models 43356, Outliers 22775, loading the pretrained vectors into the embedding matrix 10985, let move the last step which is importing the test data and apply the GBR algorithm 16444, instead of filling those values form their cluster We assume that those people don t have cabin 34050, Label Encoding of Category 6502, Submission 6791, KNN 25220, The BsmtFullBath FullBath BsmtHalfBath can be combined for a TotalBath similar to TotalSF 13425, Hyper parameter Tuning 5684, Create a new Age Category category variable from Age feature 25816, The distribution is right skewed 17032, Rare values in categorical variables tend to overfit models especially it is true for tree models 21409, Show the top performing feature pairs 28812, Train our Model 26733, Plotting sales for each category in each of the state 22054, part of our task is to teach an algorithm the following realtionship which is not clearly understandable for humans 20850, We can now process our data 15187, I know there are missing values but in order to better understand the data I create some segmentation and them I compare them with the new graph without NAN 27165, Due to the strange behaviour in Year Sold we subtract each feature with Year Sold 6911, Correlations and Heatmap 33875, ElasticNet 41936, As one might expect the output from the generator is basically random noise 37082, Findings The prediction looks quite similar for the 6 classifiers except when DT is compared to the others classifiers create an ensemble with the base models RF XGB DT KNN and LR This ensemble can be called heterogeneous ensemble since we have three tree based one kernel based and one linear models We would use EnsembleVotingClassifier method from mlxtend classifier module for both hard and soft voting ensembles The advantage is it requires lesser codes to plot decision regions and I find it a bit faster than sklearn s voting classifier 21254, Binning to renta age and antiguedad 39197, Random Forest 42255, Repeat pre processing defined previously 27983, Prepare our data for our model 10258, Separating types of features 9885, Fill Missing Age Value 33445, XGBClassifier 6705, Test Dataset 21184, How many missing values are there in the training data Some features have almost all their entries missing 27530, Display heatmap of multiple time series 9285, Joint Plots continous vs continous 5428, in the cases where the categorical variables are NaN the numerical ones are 0 19338, we have 450k Question Sets consisting of 540K separate Questions 25854, Cleaning Text 40543, Prepare data 12032, Predictive Score 38582, Our cleaned text data 609, now from here it looks more like S is the interesting port since survival is less probably for that one if you are a 3rd class passenger 36088, Writing Submission File 11174, Do some PCA for the dataset to remove some of the collinearity 5132, Numerical Variables 7961, We ll divide the data into numerical and categorical data and verify their descriptive statistics 22368, Encode Categorical Data 19392, Create the final sentences 26858, Look at summary of numerical fields by using summary function 1184, I use the boxcox1p transformation here because I tried the log transform first but a lot of skew remained in the data 17668, I use the title to find missing values in the column Age 8383, Knowing the type of our data 11554, Engineering time features 13160, Scaled Features 11805, I m going to remove the Id Ticket Name and Cabin variables 43287, Esse comando equivalente a usarmos o r2 score implementado pelo sklearn 18128, LGBM Regressor 28165, Linguistic annotations 23127, That s true Passengers who paid more for their fair mostly survived 17911, CHECKING FOR NULL VALUES IN ALL COLUMNS IN BOTH DATA TRAIN AND DATA TEST 9752, Reshape Dataset 4410, Using pd get dummies on an entire DataFrame 12163, The deeper the tree is the better it fits the training data 5333, Diplay values of variables in logarithmic axes 8332, There are many 12764, Since i dropped some features from the training set I have to drop the same featurres from the test set and do the same steps of feature eng 38562, Random Forest Regressor 22460, Dot box plot 15130, Check Survival Rate 17568, Logistic Regression 18972, Display the distribution of a continous variable with violin and boxplot 17680, SURVIVAL PERCENTAGE 34168, First let s take a look at the decomposition of the item price time series 22214, Dataset 3660, Importing and dropping ID 35835, Handling categorical features 19868, Above for first record with age 22 z score is 0 36407, We use k nearest neighbours to fill in blanks for some of the variables that might be be able to be filled in using geographically nearby properties 14745, Evaluate Again 1882, Feature Engineering 9483, AUC Curve 18881, There is a possibility that test and train datasets do not have sama NaN values especially if they are small 35582, Preparing vectors and BOW 41112, Basic Statistic using Pandas and Numpy 17012, data is a almost normal we fill missing values with mean 28129, Bag of Words 12081, Pre process 36365, Using GridSearchCV to search for best hyper parameter of XGBoost 34338, Numerical Columns 17876, Data Preparation 33139, Build a simple sequential model in Keras with just a few lines 41156, FEATURE 2 NUMBER OF TYPES OF PAST LOANS PER CUSTOMER 24812, fill the missing values 8371, Random Forest 3173, transform pandas to cudf object 18639, We have 27734 missing values and the mean age is 40 5570, At this point we dealing with correlation matrix and Scatter plot to choose best features for our model But these methods don t include any feedback to know if our choices true or not all of them depend only on native statistical techniques 25392, Train the model using data augmentation 34034, when alpha 250 RMSE is minimized 29409, We deal with the missing data a bit later 9853, Ensemble 33570, Box Cox Transformation of highly skewed features 9331, While by using the categorical variable GarageCars 2665, Quasi Constant Features 31010, we add a flatten layer that takes the output of the CNN and flattens it and passes it as an input to the Dense Layers which passes it to the output layer 36294, try on scaled data 4310, Summing up number of parents children and siblings across passengers 30834, Year Feature 13556, Categorical features by Fare 15642, Re forcast predictions based on new features 28564, BsmtCond 12269, LGBM 33275, Applying Augmentation On Melanoma Classification 14295, Cleaning Dataset 3799, Merge mutiple related or same kind of categorical features to creat a new one 12299, FamilySize 40988, To make it horizontal use transpose function at the end 13549, Embarked Feature 27780, Label Encoding 31927, Confusion Matrix 26508, To evaluate network performance we use and to minimise it is used 31811, Calculate IoU 27931, Train model 18669, Define RNN model 28257, Nullity correlation heatmap below help us identify whether missing value of one feature affects missing values of other value 32860, Removing outliers 4029, look at columns with high correlation with SalePrice 17598, Correaltion and feature Importance 26328, Try xgboost 6931, if we drop all NaN values from rows we drop only 8 of data 31344, Prepare Data 33504, Italy 878, Correlation Matrix 31941, Make Predictions 30570, we simply merge with the training data as we did before 41074, Remove URLS and mentions 8068, Kitchen Quality 10671, Assign model 20455, Client gender 24169, Learning Curves 3773, Sex 14000, Save data 13683, Embarked Did the embarkation location play any part 26024, Label Encoding 20165, Case 3 GrayScale Dimensionality Reduction PCA 7910, Inspect the top correlated features values 18347, TACKLING NON LENIARITY 15835, look at the median ages of passangers grouped by Sex class and title 13682, We can glean that children had a high probability of Survival 14195, Comparing cross val score 36613, Compute standardization of data 18841, K Means Clustering to identify possible classes 15576, Features to normalize 43027, Hyper parameter tuning 40403, Test Paths 41166, How many samples for each class are there in the dataset 27776, Calculate the Mean Absolute Error in Validation Data 22622, The Model is trained in the following steps for each minibatch 25275, Test Time Augmentation 238, Library and Data 6483, There are several additional cases when a categorical variable is None relevant numerical variable should be 0 18998, Categorical encoding 8895, LASSOCV 13660, Cabin 38032, Decision Trees 36804, We need an example to actually process so below is some text from Columbia s website 27060, Create a submission file 42190, ANN The complete example 32301, Display heatmap by count 35107, Mixup Data Generator 17465, Ticket 12820, as we know that At 2 20 a m on April 15 1912 the British ocean liner Titanic sinks into the North Atlantic Ocean 41778, Target and features 3171, Data Preparation 41569, Stage 6 Plotting the t SNE 7244, Explaining Instance 2 2 3 2 23055, after Data Augmentation its making good prediction 9939, Duplicate Values 33442, RandomForestClassifier 20301, Converting String to Numeric 15468, Data Cleaning 22472, Timeseries decomposition plot 42371, Splitting data train test 21519, Creating a Dataframe of Loss And Accuracy 15068, Fare Group 22342, N Gram 40972, This way it can take a list of column names and perform an aggregation on all of the comumns based on Store ID and Store s Volume by using Name 1 Name 2 15980, We need to divide the room feature by groups 39198, Submiss o 33725, Binning on Age Feature and Fare feature 25891, Automated Readability Index 19088, Age violinplot based on Pclass Survived 12650, all we have to do is submit the outputs to the competition The Random Forest Model gives a higher test score than the k NN model If you ve got this far please give my notebook an upvote It really helps 12352, BsmtFullBath Basement full bathrooms 10727, Feature Scaling 26554, Label Encoding 22790, look at a more practical example of housing prices problem to understand it better 31117, correlation between features 8021, Embarked 13498, Age 39702, Comparison between original text and the lammatized text 38413, Yup it works 47, Age Column 32469, Mortality Model 29219, Takeaways from the heatmap br 1247, plot how SalePrice relates to some of the features in the dataset 16249, Globals 29051, Selective Gamma correction 15123, This finds percentage of n Age class 32479, Combine Demographic Based and Memory Based Probabilities 12381, Converting ordered categorical fields to numbers 20443, installments payments 3503, look at the correlations between this new predictor set and Survived 39293, Samples of seniority 2 are made of items that have previously been sold in this very shop and so they are in the local catalogue of this shop for sure 31848, Monthly sales 5576, Lasso 23607, Evaluation Function 15549, Creating Dummy Varibles for categorical data 31829, In the code below we ll use to resample the majority class 13018, Loading the data 30925, Count the error 35392, TTA Test Time Augmentation 861, Uncomment if you want to check this submission 10267, Exploring numeric features 20297, Family Size And Alone 14923, However some of the machine learning model treat the categorical features as numeric features such as XGBoost 7539, Wait Embarked also have 2 mising values lets do filling But first we need to explore Embarked column 41845, Bi directional LSTM with Glove embeddings 15315, Lets examine the types of classes that were present 11133, we look at a polynomial fit 33351, Manually calculate the Partial Autocorrelation 37801, Residual Histogram 22913, Findings 24873, Prepare the Test Data 11114, Handling Null Values 43298, Fazer predi es com o dataset inteiro 23758, Selecting the Columns for manipulation 3961, Perform BoxCox Transformation on skewed features 27542, Display heatmap by count 27089, Show first n important word in the topics 31852, Traget lags 14987, Creating a Function with multiple models 3769, Heatmap 16399, Dropping Unecessary Data 28306, Chicking the Root mean squared error for RandomForestRegressor method 8302, XGBoost Extreme Gradient Boosting 21652, Avoid the series of lists TRAP 29773, We repeat this using the optimal model 18652, Numerical variables Vs Target variables 16461, Youngters are likely to survive more 17604, Naive Bayes 37666, Model instantiation 6208, Other SVM s 39412, Parch 28730, Hummm 26912, Handle Missing values on test data 1704, Treating Missing values 39945, The metric RMSE 42001, Sort by a certain column s order Ascending 8823, with child 0 Young Adult 1 Adult 2 Old 3 Veteran 4 8874, Removing SalePrice column 13843, Correlating categorical and numerical features 40028, Insights 8490, Stacking the Models 40140, Prediction 39188, Atualiza o h um correla o muito fraca entre as features como pode ser visto abaixo 40002, Our test set misses three columns diagnosis benign malignant target 1058, We check next the important features that our model used to make predictions 18592, create a new data object that includes this augmentation in the transforms 37526, Embarked Sex Fare Survived 19858, The boundary using times the interquantile range coincides roughly with the boundary determined using the Gaussian distribution vs years 15469, Data Cleaning Extraction of Meaningful Data from Meaningless Data 26521, Distribution of product among different genders 2767, Description of new features 9948, Hetamaps 937, Meet and Greet Data 38766, XGBoost 27184, on the leaderboard Score 1515, Very interesting We found another strongly correlated value by cleaning up the data a bit 13490, Concat data 28850, Load Libraries 24732, Predictions 17566, Female passengers have survived more than male passengers e Females and Children would have been the priority 39010, Here we have looked at the ensembling however the final score can be improved upon with better feature selection as well as both feature engineering and hyperparameter tuning of the individual estimators 18686, create an ImageDataBunch object 9346, Predict the missing data in the Age feature 16690, Decisions Taken 14112, center CountPlot center 17744, However this mode would assign the NaN values the wrong value of C 30587, Aggregated Stats of Bureau Dataframe 786, ElasticNet 37482, spaCy can do a lot more but for now we are going to turn to sklearn to vectorize the lemma version of the sentences 4877, Light GBM 269, It s an important method for dimension reduction It extracts low dimensional set of features from a high dimensional data set with a motive to capture as much information as possible and to visualise high dimensional data it also reduces noise and finally makes other algorithms to work better because we are injecting fewer inputs 19372, Addressing outliers 25579, MSZoning 14439, go to top of section eda 21135, These were quite simple let s look further 23125, Findings The distribution of Fare between different categories of Survived and are distinct very least overlap that makes it comparatively strong predictor for Survived what is kind of true from the correlation value of and the p value less than that suggests we re confident that this correlation is statistically significant survival is positively correlated to Fare so the more you pay for fare the more your chances are to survive that is quite evident from the box plot 9282, Carryout univariate and multivariate analysis using graphical and non graphical some numbers represting the data 38078, Lets take care of remaining columns with missing data 21021, Lets create a corpus of all the tweets in the training set 13337, Ticket extracting information from this feature and converting it to numerical values div 15722, K nearest Neighbors KNN 14939, Correlation 15702, The histogram tells us that most passengers have ages between 16 44 12095, DataFrame concatination and Y separation 8766, Handle NAN age 34744, Taking our sample text corpus 22282, One Hot Encoding one of the most useful techniques that a data scientist can know 42182, Train the model with fit method 39981, We fix this later 30923, Our predicted values 40338, We haven t shuffled test to we can just create the submission as follows 36020, Ensembler 28186, idf W log documents documents containing W 6676, Bagged KNN 8980, Similarly I noticed that the features Condition1 and Condition2 are also dependent of another so I want to split their values into their own columns 26115, select numerical and categorical features 39156, Prediction 29617, Bivariate analysis 16060, Fare Vs Survived 26959, merge data into one dataframe and extract feature from date colomn 2976, We ll consider that when more than 20 of the data is missing we should delete the corresponding variable and pretend it never existed 8319, Choosing the regression model SVR with lowest RMSE and performing hyperparameter tuning using RandomizedSearchCV 31675, Data Preprocessing 33233, now the extracted features are stored in the variable resnet features 35131, adding an additional feature that records the no 20946, Import Keras layers 39823, let s call this image gen we created 41355, There is a correlation between BsmtCond and SalePrice 14855, To further summarize the previous trend as my final feature I created four groups for family size 15787, This is our X train 7358, Drop noisy features 11471, Gaussian Naive Bayes 39432, tensoflow neural network insurance claims 0 268 31089, TotalBsmtSF font 9173, I feel like that helped 21367, To find more noise you can run the following code to get random samples 4792, For features that can t be missing we have taken the mode and the default value as per data description 12621, create dummy variables 22904, WordCloud 40420, Word Clouds 4097, Encoding 29042, Randomly pick 5 images from each of the five labels 12677, Model 1 3643, Cabin 15907, Ticket short with Survival 33440, LGBMClassifier 14289, Draw the decision tree using graphviz 23721, we perform the same steps for Test Dataset as well 29545, Most common words 39240, Remove outlier shops 2546, center Conclusion 15063, Import data 21924, XGBoost parameters from reference notebook were hypertuned using CV but it took long time to run 1306, Stripplot 2188, Visualising missing values 19444, Using a 4 layer neural network with 22856, Russian Ruble Price per month 24890, Random Forest 4666, most correlated features 9029, I took the average of BsmtFinType2 values within 1 SD around the BsmtFinSF2 value 3624, Create Binary Variables 20601, Name 19698, Data Augmentation 41701, Somewhat weird trends looks like some of the top places only opened up business part way through 4235, Random Search 9317, In summary the lesson here is that we can t give an ordering to something that is not supposed to have one as in the previous section but when we do we should keep in mind that we are making an assumption when we put numerical values in our data As such we should keep track of our choices and how the model is reacting to that 38776, Submission 3277, Check again if we have any feature left with missing vale 16618, Sex 23311, Previous Value Benchmark 23084, STEP 3 Model and save your prediction 9942, Creating features that the person is married or not and the family size with SibSp and parch column 6044, try Blending our Models 12319, Preprocessing 34429, Not Disaster 2720, Support vector regression 6527, Create Dummies 27269, When dealing with continuous variable how do you calculate the probabilty for a given value The probability should be zero 32874, Modeling the data 7661, output predictions 8287, Viewing Model Performance 1418, Mother vs Survived 20294, Fare 2230, Which Material Combination increased the Price of Houses 33719, We know that Cabin and Embarked have missing values 22449, Lollipop Chart 2808, Matrix 28959, examine numerical features in the train dataset 16837, Making predictions on the test set 21410, Create Test output 8547, Basement Features 10137, Predictions 3960, Skew Visualization 9958, Voting Classifier 15176, Age 1751, Comparing the KDE plot for the age of those who survived before imputation against the KDE plot for the age of those who survived after imputation 24418, Timestamp 14190, GaussianNB 30388, Making Data for the network 1778, Confusion Matrix 23462, Training set 21336, Feature Engineering 31538, GarageYrBlt 12907, Import Libraries 9434, Displot 15727, Random Forest Feature Importance 10533, Skewing the features 18957, Display distribution of a continous variable 20754, Fence column 37322, Select the number of convolution layers 19805, Classic Encoders 15251, Feature impotance 31353, Interactive Pandas plots 32958, Define accuracy metric 4684, Rows are precious for House price prediction fill the one SaleType missing value with the most common value of SaleType 37011, Best Selling Aisles in each Department number of Orders 22781, Point Type 5964, family size feature 42360, slicing back into train and test 26987, 80 20 train validation split 6865, Dealing With BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Please notice the dependency 39326, Load Embeddings 24590, PIPELINE 11521, Gradient Boosting Regression scores 37980, Early Stopping in Keras 3314, Lasso 40005, Patient id counts 19066, The evalution requirement in this competiton is that for each image name in the test set you must predict the probability that the sample is malignant 21545, Basic CNN model 22978, Correlate based week of month facet per DayOfWeek 2952, Predict SalePrices 32423, Understanding how the Network Works 34403, Create the model and compile it 38067, NLP Features distribution conclusions 39419, replace the NaN value in Fare in df test with the mean value 503, People who are alone are more likely to survive 17629, Title 33314, Plotting Decision Regions 38593, Final Predictions 24962, Kernel SVM 19258, Transform the data into a time series problem 39028, Learning curves 21431, Here train 15517, The change of Age as a function of Title Fare bin or SibSp is quite significant so I ll use them to guess the missing values 18392, Training of the neural net 20415, Feature word share 10372, Relation between categorical features and dependent variable 40136, Export to CSV and upload to GCS 22872, Model Definition 12676, Modelling 30361, Predict all province greater than 500 4274, Continuous Features Nulls 29567, Non correlated cols are removed 16836, Precision and recall tradeoff 17797, Estimate age 11969, treat categorical and numerical features seprately 32738, Encoding categorical features 25238, Barplots display 4382, Generate New Feature 35811, Shop features 20322, Section 3 Preprocessing our Data 9039, much better The data points are now fairly symmetrical and there isn t as many outliers on one particular tail 11994, plot the dependence contribution of features on sale price 29860, Accessing the data element by 14269, Gaussian Naive Bayes 17262, Cabin Feature 43333, Make Submission 13800, I also follow some old methods of data analysis to increase focus 27615, Test Data Preparation 34262, The lags and rolling windows created Nan values 33883, dropping features with small variance 27399, Convert images to tensors 24819, RobustScaler Definition 41659, From section 3 30916, Simple bidirectional LSTM with two fully connected layers We add some dropout to the LSTM since even 2 epochs is enough to overfit 27380, working on the lags 32854, EDA 38760, Support Vector Machine SVM 39185, N mero de crimes que ocorreram durante cada m s e ao longo dos anos registrados no conjunto de dados 11506, Setting for K fold cross validation 11734, compare how the model performs on the test dataset 4399, do some feature engineering with the name variable 38687, After Mean Encoding 32412, Preparing data 36624, Model Performance Analysis 11822, Lets create a correlation matrix using heatmap 7445, Logistic Regression in Python 6252, Fare 4338, Fares on the first class range from all the way to Although a number of tickets were sold at a much higher price x to x normal price than the average price of approximately Could they have been sold at a higher price closer to the sail date 7757, Pipeline for numerical features which aplies an imputer to put median of each feature for instances which doesn t have value for that feature and after that standardizing features using standard scaler 3872, How did we generate scores 22148, You could try to optimize those weights in some other way 25444, Training Dataset 22288, Check nulls 37411, There are now no 0 importance features left 10380, Lets make a plot to intuitively understand what s missing in our data and where it is missing 33254, Categorical features 4768, Replacing the embarked column with the most frequently occuring value in the Embarked column since there are a really few na present 20935, Plot an example 25799, As mentioned before fortunately these top contributors are the same for both datasets 42, How Big is your family 14223, Title Map 46, Convert Categorical variables into Numerical ones 14114, center ViolinPlot center 5310, Univariate feature selection 26000, Creating Columns 20838, We re going to set the active index to Date 10375, Data Pre processing 5, focus on numerical features with one missing value and replace them with 0 10682, NO MISSING RATIO 39195, Normaliza o 34284, multicollinearity 12660, Data Pre Processing 41781, The labels are coded as integers corresponding to the digit in the image 16018, This is survival rate each SibSp and Parch how about join them together 41250, The features created below are known as Indicator Variables Raw data is transformed into a much more straight forward form of input hence allowing the ML model to easily recognize these features also making the model more robust In other words we can also classify this as revealing insights 29897, Feature Standardization 23343, Callbacks 7852, The Random Tree Regressor A Terrible Model 28733, Understanding the revenue for each Shop Name 15901, Cabin 36630, Features contains a big list of features is there anything unique Can we build a relational database on these features 27944, Create a Submission 422, One of the figures we may find interesting is the one between TotalBsmtSF and GrLiveArea 29389, final neural network prediction model span input layer span nbsp nbsp the input layer for the final Neural Network is the same as the flattening layer s output that is the flattened vector hidden layer span nbsp nbsp hidden layer of n3 64 nodes and activation funtion reluoutput layer span nbsp nbsp nodes number of classes 10 and activation funtion softmax 31416, Training and Evaluating the CNN MNIST Classifier 14302, Creating a features Family size corresponding to sibsp and Parch 18097, Here we use the auto cropping method with Ben s preprocessing as explained in 11418, Use case 10 US Store Sales Exploratory Data Analysis 830, columns and correlation before dropping 13274, Download datasets 40011, Image location 40014, Ages per patient 4178, Outliers in continuous variables 23310, Insert missing rows and aggregations 17043, Mean Encoder 555, submission for svc 35903, Submit 37558, How many files loaded 38547, Average Length of Sincere vs Insincere 4778, RandomForestClassifier 28101, Removing the not needed ID column 24797, CNN 3917, Correlation 5601, Garage 28782, I was not able to plot kde of jaccard scores of neutral tweets for the same reason thus I plot a distribution plot 1236, Importing learning libraries 425, Concatenate both train and test values 13070, Logistic Regression 7983, Merge BsmntFS and add Unfinished Fraction 14246, We saw that Sex and Class is important on the survive So Lets check survival rate with Sex and Pclass Together 34065, Parch Survived 25206, Check the top 10 and Bottom 10 correlated and non corelate features respectively 14616, Station 3 Exotic impact 34968, We are going to get an age estimate for 5442, Tree Drawing function 13230, Feature Importance using Permutation Importance importance 14109, center RegPlot center 31926, The calculate performace function calculate the accuracy and gives back data about the falses of the model s predictions to visualize and analyze them 28062, Feature Engineering 13492, Others 35306, CNN 7080, we fill the missing Age according to their Title 902, Create 3 functions for different Plotly plots 31738, Class Imbalance 9592, Concanat to get P Class details 11018, We cannot process the age as a whole number 39189, Extraindo features a partir dos endere os 5888, Fitting for test data also 37706, Accuracy was 67 and now it is 90 33728, Fit Model Using All Data 28614, Exterior 19715, Fit the model 17459, Family 12890, Fare 11888, Decision Tree 31308, Average number of customers across all the stores every day is 633 27365, working with shop name 34676, Sales distribution by item category 30374, Cosine Annealing 2268, Sex 39061, Glove Embeddings 11886, ADABOOST CLASSIFIER 15481, We leave out Name Ticket and Cabin because the syntax brings about no direct insight to the survival rate 17562, No missing values left so we can proceed further 2350, Light Gradient Boosting 2575, Model and Accuracy 31243, Kaggle Submission 14874, Look like survival rates for the 3rd class are substantially lower But maybe this effect is being caused by the large amount of men in the 3rd class in combination with the women and children first policy 11215, Plot prediction vs actual for train for one last verification of the model 26570, We need to know the shape of our images 39262, DISTRIBUTION OF SALES AMONG SHOPS THROUGH TIME 16527, read the data both train and test one and to make sure that they are in the same format we for the time being concatinate them so that both have the same operations 12833, Feature Engineering 29007, CNN 41833, Confusion Matrix for ensemble models 36803, Using spaCy we ll load the built in English tokenizer tagger parser NER and word vectors We indicate this with the parameter en 27116, let s examine categorical features 23075, Fill Cabin 27271, Calculate mean sd of train data for each each feature 1123, I ll also create categorical variables for Passenger Class Gender and Port Embarked 1358, In pattern recognition the k Nearest Neighbors algorithm is a non parametric method used for classification and regression 38624, Used it upto version 7 40018, Individual patient information 21120, First we look at some global measures of our data set 19455, In artificial neural networks the activation function of a node defines the output of that node given an input or set of inputs 30105, PREDICTIONS 20596, Count Plot for Features 14904, Embarked Fare and Pclass 12035, MSSubClass is not a numerical variables so let s transform it to caterogical variable 23845, That look great the new features engineered have outperformed the existing features in the data in the RandomForrest feature importance plot 20808, Dealing with Data for Modelling 9841, Which is the best Model 5175, Linear SVC is a similar to SVM method Its also builds on kernel functions but is appropriate for unsupervised learning Reference Wikipedia vector machineSupport vector clustering svr 6688, Scatter Plot 22531, SibSp vs Survived 17270, Prepare data for train and test model 39311, XGBRegressor training for predictions 23509, There are 5 elements in the class 16523, its looks good 20919, Discount rate 15750, We may take Age median Age of passengers with same 5582, afterward Salutation column should be factorized to be fit in our future model 18906, SibSp Feature 6303, Support Vector Machine 29873, Optimize the Metric 8358, Correlation of the variables 27478, It would be possible to train the net on the original data with pixel values 0 to 255 10268, Observations 2527, AdaBoost Adaptive Boosting 17691, INITIALS AGE GROUP 11836, Additional Missing values in test data 51, Logistic Regression 27182, Final prediction with XGBoost on test dataset 1832, Columns with NaN Values 13534, Liner Regression with Autofeat from kernel Autofeat Regression feature engineering comments652596 33651, Final Submission 549, SVC features not scaled 43364, Submission 7052, Type of foundation 32504, Extracting VGG19 features for training and testing datasets 36644, Class 0 is fish and class 1 is no fish 23382, random sized crop The model needs to be able to detect a wheat head regardless of how close or far away the head is to the camera To produce more zoom levels in the dataset the crop method take a portion of the image and zoom in to create a new image with larger wheat heads 40805, Data Analyze by visualization Method 37000, Period of Reorders 32179, Make dataset 42652, Prediction and Submission 25804, Splendid 30707, define augmentations using albumentations library 10062, Correlation matrix 19576, Handle Duplicate Shops 39784, Whenever the logerror of one is zero the other is not This pattern is consistent with other nearest neighbors as well 5396, smart way to impute Age Refer to to prediction dietanic notebook 42047, Replacing column heads 14192, DecisionTreeClassifier 42971, Exploratory Data Analysis 35199, Ridge Regression L2 Penalty 41346, There are no missing values anymore 23874, Gradient boosted decision trees are the most powerful and widely used models for supervised learning 4253, Ordinal Features 4730, We can use the info method to output some general information about the dataframe 1509, lets take a look at how the housing price is distributed 2902, GradientBoostingRegressor 27613, Vgg16 13309, K Nearest Neighbor 15867, Create Random Forest model 4634, Sorting columns 41037, build an ensemble again 43383, And we pass it to our class and call prepare 37943, Geographic Spread of Covid 19 34867, we import the embeddings we want to use in our model later 4325, SibSp Siblings and spouse count brother sister husband wife etc 25718, Data Visualization 6421, Outliars Detection 28495, Train the model 34047, Create Hour Month and Year Columns 20543, XGBoost Regressor 19266, CNN LSTM for Time Series Forecasting 15380, all titles are in shape 36748, Creation of X train and y train 33650, Model Comparison 34345, Choose Algorithm and Fine Tune 1255, We use the scipy function boxcox1p which computes the Box Cox transformation 16152, Fare 24007, Lets visualize some train examples with it s labels 26571, we get to the model itself 23402, There also appears to be a weekly structure to the date y variable although it isn t as cleanly visible However the class probabilities appear to swing much lower reaching on a weekly basis 41466, Verify we do not have any more NaNs for Embarked Val 33718, FEATURE Embarked 16321, NOTE 41082, Roberta Base Model 12178, Passengers of higher classes have better chance of survival 8988, I am also interested how the number of stories affect Sales Price 38236, Pre print Analysis 4352, OverallQual Rates the overall material and finish of the house 16614, Feature Age 4700, We can predict the LotFrontage missing values with a R 2 of 0 559, submission for decision tree 17416, Producing the Submission file 32543, Feature Engineering is a technique by which we create new features that could potentially aid in predicting our target variable which in this case is SalePrice In this notebook we create additional features based on our Domain Knowledge of the housing features 2320, Fitting a regularized linear Model with k Folds 43128, Lets submit it 5365, Diplay charts with date range slider option 15988, Support Vector Classifier 10827, start from checking values in Ticket category 32563, Diff Sex 14679, Removing name ticket number and passengerid as they are not important features in predicting if a passenger die or survive 1780, Choosing Cross Validation Method 20949, Data Preprocessing 31705, Modelling 18749, Aggregating 16956, Sibling Spouse 40489, XGBoost 33858, Featurization through weighted tf idf based word vectors 39746, Modelling 13054, Random Forest Classifier 25575, OverallQual 19059, Specify the evaluation metric and check how many labels there are 34639, Data Visualisation 32703, function to remove emoji 38962, In order to select the best model epoch weights we need to create a valid competition metric 3915, check for any ramaining messing values 1207, Ensemble methods 12520, now we understand data with the help of visualization techniques 33471, COVID 19 global tendency excluding China 32158, Brilliant isn t it But we have a problem 3372, I just run those functions on my train 9329, From continuous to categorical 12173, Buiding Pipeline 21234, Set initial bias 33266, Prediction 16504, Model Evaluation 15726, Compare the chosen machine learning models 22814, And just to satisfy my curiosity bowels there is only one item under item category 71 and it is indeed present in the test set 29033, Save clean datasets 13741, AdaBoostClassifier 31636, PdDistrict 9225, Train the Random Forest 15419, let s have a look at the age of the passenger 43057, For example var 0 var 1 var 2 var 5 var 9 var 13 var 106 var 109 var 139 and many others 4626, Distplots are histplots with curves on them 22507, Below we are going to divide data into Mini batches and Iterate over many times to get global minimum cost 216, BernoulliNB 3415, Crosstabs to check for correct totals 23229, we convert the shape acceptable to our neural network and y test and y train into numpy format 24849, Preparing the inputs and shuffling the dataframe to make sure we have a bit of everything in our training and validation set 30110, Our library assume that you have train and valid directories 18904, Age Feature 13314, Solution 7104, k Nearest Neighbors 13736, KNN 13565, Ploting Title Distributions 1234, Preparing the data 4181, Age 22908, TD IDF Count 29388, flattening layer span nbsp nbsp we now add the flattening layer 32123, How to find the correlation between two columns of a numpy array 20609, Fare 3549, Embarkation 24816, labelEncoder convert categorical value to numeric 39173, index 27470 604, We study the correlation of Age with Pclass using a violin plot which is also split between survived and not survived 23653, EDA 13973, Pclass vs Sex 8247, Inspecting Data 25205, Analyze Numeric features for correlations with Target variable 20221, Evaluate the model 33772, One Hot Encoding 19961, Simple modeling 3977, Examine model performance 15651, K Nearest Neighbors 18341, For this purpose we are going to use kruskawallis test which is also known as One way ANOVA with Ranks which is a non parametic test use to determine if there is statistical difference between two or more groups 22445, Diverging dot plot 15386, I added a condition that femalechild should be non solo traveller 25671, Set predictions 1 to the model s predictions for the validation data 26195, Masking all except Lung and visualization 12197, We can use these transformers in many places but the imputer probably come before the others as we don t want to deal with missing values again 1813, Creating Dummy Variables 40970, Looking at the average price for each store 29783, Decoder 4163, Variable Transformation 38955, Preparing the Data 1960, Both the test and train Age features contains more the 15 of missing Data so we are fill with the median 1342, replace Age with ordinals based on these bands 32401, Weighted Avg Bayesian Optimization 34691, Number of active days for a particular item shop pair 4727, let s try printing out column names using columns 27641, First we have to read the train and test data 19966, Ensemble modeling 36483, that we have a list of properties for each file we are going to look at we create smaller files for each of them 4250, Continuous Features 25745, Alright now we can check whether the shape is enough to say which source the image comes from 32660, Categorical features with missing values are identified 5586, Detecting the missing data in test dataset is done to get the insight which column consist missing data 4160, Target Mean Encoding 31057, PAN 18099, Since we want to optimize the Quadratic Weighted Kappa score we can formulate this challenge as a regression problem 40701, Defining The Regression Model 16997, Prediction 15475, Imputation filling of empty cells of the Dataset 16582, Bravo Mrs 28852, Read Date 14175, I repeat the same process but with some columns being removed 18391, Definition of the seq2seq neuronal architecture 18022, combine ticket groups and name groups 8925, Alley 9101, and now I can drop MSSubClass and HouseStyle 17952, Support Vector Machine Model 16386, Sex 0 Titles 3 e 20801, Create TotalBath feature 419, Correlation Analysis 31439, A different type of model Random forests 38240, make a report of all the data then we dive deep into each column to understand in depth 39065, Observations 27779, Reshape 38066, NLP Features distribution Train vs Test Analysis 33087, Putting everything together 40463, Heating Cooling 6022, First Import Data 39996, Blending with top kernels 41493, Import Libraries 32796, LigthGBM K fold OOF function 16834, Precision and Recall 33765, Plotting Random Images 9630, Neural Network 4845, Dummy Encoding 9347, Split data in to training set and to be predicted set 3232, Chloropeth in texas 12925, Pclass 13265, Statement Women all survived and men all died 18971, Display the distribution of a continous variable 32363, Imputing Missing Data 34402, Reshape the data 20034, plot the first 5 images from the dataset to better visualize the data 28338, Analysis based on TYPE SUITE 7920, we re gonna LabelEncode all quality features where the order matters and the precedents columns 31240, Build roBERTa Model 16016, 177 null value 31683, Constructing a very simple encoder and decoder network 10771, Pre processing Pipeline joy 18478, Findings 6675, Voting Classifier 16284, Built in plotting 6641, Total number of survived and not survived 20260, Linear Regression 36576, Out of curiosity what are the labels associated with the hottest apps No Pok mon Go yet 15643, Make revised submission 36556, At least one big gaussians with one or two small very thin but long gaussians 993, remember what happens with missing values obviously the actual numbers and percentages be different since we have a concatenated dataset now 11799, LABEL ENCODING OF CATEGORICAL FEATURES 29807, Pretrain Glove 19055, However before we can create the DataBlock so that the images look real we need to create a new method so that PILBase takes into consideration the Photometric Interpretation 18965, Display the density of two continuous variable 34649, Parsing dates 10457, fill in the holes with the means on numerical attributes 7596, Visualizations for Categorical features 586, Blending Models 22716, Creating PCA models 6862, We Drop the following Columns because they are strongly Related but btween them I chose the one with stronger relation with Saleprice 4818, When performing regression it is always important to aim for normality that is does our dependent variable follows normal distribution 37198, Building Model 15454, Embarked 20480, Credit type 27782, Initializing Optimizer 6672, Kernel SVM 35909, Convert data into dataframe 17998, I have noticed that using the feature Embarked does not improve my model so I discarded it as well 37047, Applications of lemmatization are 14110, center Joint Plot center 6939, Target 13363, Print variables containing missing values 6896, Looking at the Age distribution for men and women it s clear that the average age for both was about 30 32351, Data for the Network 34401, Normalize the data 36370, Data Augmentation 37895, Best Score and Params 7489, We group the ages in 5 groups weighed according to the survival rate 28688, SaleType 41048, Order By Days 16477, Class 33326, Save the model 15040, Cabin 15158, Lets predict the test data output using random forest classifier 15492, Optimization Algorithm 11095, Base Model 23358, Learning Curve 21193, Linear Activation Forward 23719, Extensive Hyper Parameter tuning using Hyperopt Library 20466, Credit distribution 39447, installments payments data 9513, Embarked 7979, Model evaluation 6595, The ROC AUC score is the corresponding score to the ROC AUC Curve 3952, Create TotalSF Feature 23377, Clean bounding boxes 28566, BsmtExposure 16438, Yup Values differ totally according to Pclass 8990, Yeah I ll keep this feature 24162, Build Validation Set 19008, Build the final model 42962, Title Feature 11058, Fare versus survival 35940, Hyperparameter Tuning 12874, Via input layer data go in from output layer we get the prediction from our neural network 14476, now inference with respect to survivebiltity and age factor 40966, This step converts our dataframes as passable to the Nueral Networks 19980, MLP with Batch Normalization on hidden Layers AdamOptimizer 2 13136, VERDICT WITH BEAR GRYLLS 19646, Age distributions by phone brand 9006, convert categorical ordinal variables to integers 4180, Age is quite Gaussian and Fare is skewed so I use the Gaussian assumption for Age and the interquantile range for Fare 3865, Feeling a little lost 35872, Confusion matrix is a great way of visualising the performance or rather the shortcomings of our model 24298, Train Test Split 4923, Create the Folds for Our Cross Validation Model 38938, State Holidays 38626, Inference and Submission Generation 9857, Following codes give us whole Sex column 32287, Display distribution of a continous variable in population standard deviation boxplot 1791, Observation 13496, Family Size 4647, Combine Violin Plot Swarm Plot 15455, Family Family Size Family Name Family Survived 26074, Reading data 577, Compare feature importances 8103, Pclass 34861, I m trying to find features here that influence the survivablity of a passenger 9650, Lets find out the percentage of missing values according to which imputation and datacleaning can take place further 37701, find minimum of this function using gradient descent 24974, Ensemble of different models 14380, Lets check thaht people with cabin were more likely to survive or not 27277, Using more accurate kernel function we can achieve 0 15725, Random Forest 2721, Lasso regression 21384, We obtain 99 3 acuracy 1320, creating matrices for feature selection 26805, Augmentations 42961, It s time separate the fare values into some logical groups as well as filling in the single missing value in the test dataset 8976, finishs feature engineering and null values part 27937, use the model to classify each image 14913, Age 5125, Random Forest Classifier 38677, Image Size 16017, SibSp Parch 27118, As there is very less data available for these four columns we can drop them 12042, imput missing values using two functions which I wrote 26025, Data transformation is very vital for any data that contains numeric variables as it may have Positive or Negative skewness 12615, replace the NaN by mode 15194, Tuning Model using RandomizedSearchCV 27532, Display the variability of data and used on graphs to indicate the error for different categories 7012, Type of utilities available 26824, check now the distribution of standard deviation per columns in the train and test datasets 14797, Hyperparameter tuning for Decision Tree 15598, Survival by Age Class and Gender 24773, Load Libraries Data Tokenizer 2895, Categorical Features Encoding 32077, Categorical Variable Summary Statistics 20818, We ll be using the popular data manipulation framework pandas 28303, Conver categorical string values to numeric values 21549, its time to evaluate the model 7143, Logistic Regression 8237, Object to Category 20388, Decision Tree Model 28857, Split Data 30148, temp and atemp have high correlation and register and have too 20668, FILLING MISSING VALUES IN MSZoning 18334, OverallCond 17363, Stacking base learners 35753, Import libraries 38697, Probability of melanoma with respect to Image Size 3313, XGB 20079, Insights 25840, Emojis Text Cloud 35110, Additionally we also need to compute p e 34270, Training 14283, Applying model with default values 20356, we are ready to submit to Kaggle 416, Drop the Id column because we dont need it currently 41032, The fold precision of the ensemble is slightly better than and in between the original precisions of the three models 27291, Fit the SEIR model to real data 17972, SibSp and Parch 18535, Examine the predictors 42778, Visualize the numbers 20876, At first we read in the data 10255, Go to Contents Menu 16589, Lable Encoding 16759, Families or Alone 6926, Preparando arquivo para submiss o ao desafio 39822, I am using ImageDataGenerator function from keras here for data augmentation and have set the range for different features as below 41336, To check if our predictions make sense we check the target distribution of the training data with the distribution of our predictions 24795, RNN 23977, train with small image size first to get some rough approximation 29569, one hot encoding 33309, Feature Selection by Recursive Feature Elimination 25296, I consider this beginning part done 26859, Look at summary of numerical fields by using describe function 34520, Entities 15495, Make Predictions on Test Set 32602, Random Search on the Full Dataset 7004, Above grade living area square feet 39382, Dumping ult fec cli 1t and conyuemp 38542, Its important to notice that there is no sense in keeping high depth values 6095, We can do more investigation on other variable outliers at a later date 9601, Data types 23257, Passengers with 1 2 Siblings Spouses have a higher chance of Survival 34690, Borrowed from 34716, Model 8305, Model 2 Random Forest 33335, FEATURE 8 AVERAGE DRAWING PER CUSTOMER 28201, Chinking 32261, Grouped Bar 32946, Get features len common word count percentage of common words seqratio similarity 635, Large Family 15650, Gradient Boosting 3629, Combine some features 40149, Missing values 4850, Gradient Boosting Regression 23253, Age Cabin have significant rows with missing values while Fare Embarked have a few rows 14574, Importing the input csv 20252, I have used GridSearchCV for tuning my Random Forest Classifier 3801, Models 4729, lowesst 5 7431, Basically grid search considers all the combinations of parameters so it takes longer time than randomized search 35241, We need to what sort of words are place on top for each sentiments 1226, Deleting features 39839, Loading the dataset 7660, We need to apply np 13145, bin up using pd 21259, Drop date and items to get user information 30371, Inception Model 32336, log error changes based on this 25320, Run the code cell below without changes 27330, Looking into data 39999, At this stage I compare two data set according to the Pclass 30599, The highest correlated variable with the target is a variable we created 2091, Get insights from the data 14255, There looks to be a large distribution in the fares of Passengers in Pclass1 and this distribution goes on decreasing as the standards reduces 39418, get rid of columns with NaN in Embarked in df train 14516, Observations 30258, Model validation 1030, But before going any futher we start by cleaning the data from missing values 20728, MasVnrType column 7264, Age Feature 18292, Embedding Matrix 26926, make a list of sentences by merging the questions 11111, Explore Data 31559, Prediction 13750, Split data into training and test set 2970, Univariate analysis 15505, It looks like there are two types of tickets number letters number 41251, ANALYZING THE OBSERVATIONS WITH MISSING DATA 43297, Testando sem a Coluna Month 41571, Importing Various Modules 12034, Fixing variables 10421, FE EDA 4397, Feature Scaling 10559, Create separte datasets for Continuous vs Categorical 1794, SalePrice vs OverallQual 18365, Checking Outliers 34323, Divide the labels according to label stratification 41338, Basic Relation Analysis 32794, Xgboost K fold OOF function 18122, Data Prep for ML 3352, Same for Test dataset 18756, To visualise the slices we have to plot them 5342, Diplay quanitive values of a categorical variable for multiple terms in stacked funnel shape 6195, Submission 2 for SVC without Hyperparameter Optimisation 18323, we try lightgbm with different parameters 16145, Name 43178, Simple CNN 24363, Certainly a very long right tail Since our metric is Root Mean Square Logarithmic error let us plot the log of price doc variable 19549, Simple Naive Bayes on TFIDF 10167, Violin Plots 33774, Compiling the Model 11749, There are still a few more missing values in different columns 30565, Kernel Density Estimate Plots 33750, Take a look at what we predicted on a test sample 38234, Conclusion 4468, In the following funcion the medians of each group would be used to replace missing values in the Age based on their groups 6638, Modelling 16571, Evaluation of model with 3 classifiers 33239, predict the output on new images and check the outcome 19625, Finding null columns though PyCaret manages to fill null values 37393, Done check what s going on with GrLivArea 29424, let s train our model 39315, Save model and test set 6796, Definindo vari veis 11761, Understanding our Models 11608, From info we know that 15270, Gaussian Naive Ba Algorithm 40879, All of the model are doing okay in terms of bias variance tradeoff except kernel ridge just a bit of high bias or low variance and hence underfitting Since training and validation curves haven t yet converged adding more instances might help for lasso ridge elastic net and svm And for kernel ridge increasing model s complexity perhaps adding more features might help 30612, Test Two 34695, Projected values silly linear extrapolation to the next month 23394, Evaluate Model 34004, workingday 34324, Confirm that the labels of both training and validation sets are equally divided 37873, Train Test Split 27921, Underfitting and Overfitting 40191, Read Data 8772, We need to group fare by each 50 dollar 29795, Skip Gram 9725, Onto multivariate analysis 31585, Continuous Features Importance 3407, we ll look at a frequency table for the target variable Survived 32872, Replacing missing values 40310, Train and predict 37166, That is really powerful stuff In just 5 epochs we reach 0 24681, Submission csv Generation 40771, Build Model 19, RandomizedSearchCV Ridge 7028, Fireplace quality 5119, Missing Values Treatment 13792, Random Forest 43, Do you have longer names 25464, Initializaion 27203, Sex 15249, Feature importance 30948, Data augmentation 35929, Pclass Sex 21747, Somewhere around item id 6000 we might have an outlier 6468, Get the labels 32766, We use data augmentation is called in place data augmentation the process is input batch of images to the IDG and transforms each image in the batch using random translation at the end the transformed batch returned to the calling function 25811, Prediction 21820, Non Numerical Features 12504, From the cross validation we re getting that the best subsample and colsample by tree values are both 1 13960, Number of missing values 10370, Continous Variables 21504, Image with the smallest width from training set 15042, We could conclude that 38030, Performance Function 11538, Preprocessing 28874, Transformiation 2673, MI for Classification 7124, Parch vs survived 3944, Numeric variables Imputation 25653, Use the next code cell to preprocess your test data Make sure that you use a method that agrees with how you preprocessed the training and validation data and set the preprocessed test features to final X test 3273, Fill these BsmtExposure null values with values of BsmtQual and other with None 29984, Create output 462, Filling NA s in numeric columns with medians 38786, Find mode for each case 39029, Prediction 15003, Rather than calculating the percentage of passengers by hand we can use the built in parameter normalize True within our value counts method call 6713, Explore the Numerical Features 25766, Pclass id for define Ticket class 10413, Sum multiple features together to get overall totals 40075, Save dataframe in case we use another model which don t require the steps necessary for linear model 34319, I m gonna label the data so that in train test split I won t have to use stratification 42662, For the difference of the missing value correlation matrices a striking pattern emerges 12198, Which can be tested very simply 8596, Building Numerical Pipeline 10513, We have a few Null values in Test Age Fare let s fill it up 17682, PASSENGER CLASS SURVIVAL PERCENTAGE 16169, Analyze by visualizing data 42786, Spot checking some values 30573, None of the new variables have a significant correlation with the TARGET 8104, Name 8395, Adding some interesting values 37532, we need to pad our input so it have the same size of 512 31026, Number font 16898, Nobles all survived 37405, Remove Missing Values 8828, Feature Engineering Scaling 39748, I m going to use a function from the model selection module in sklearn 5436, Fence 19981, MLP Dropout AdamOptimizer 38836, The model is able to classify almost all images 8399, TPOT is very user friendly as it s similar to using scikit learn s API 18974, Display more than one categorical variables distribution in a parallelized view 32609, Categorical Nominal Features Exploration 20644, CNN with word Embeddings 33231, Comparison of different architectures 4068, we can split our variables in training set training labels and testing set 24274, Linear SVC 36017, Preprocessor 19423, And with the Dataset defined we can get the train and validation DataLoaders 43092, Model 29834, Input Dataset 3781, Model 2 12635, Removing Unfillable Values 38822, And we create a list of the feature names if we would like to use them at a later stage 39316, Import and process training set 3401, Meet the Outlier 19859, The boundary value using the 3 times the interquantile is a bit high according to normal human life expectancy particularly in the days of the Titanic 28719, Quantiles of continuous features and target 39777, that s a beginning Lets work with predict proba to find the best threshold that optimizes our f1 score for insincere questions 20534, Setup cross validation 27657, Model Definition 9726, start by looking at LotArea and its relation to LotFrontage 17799, More features engineering 32912, These tools are generators 38847, Even most of the houses are 1 family but it contains 2 car parking garages 5856, Skewed variables 23718, Splitting into train and test DataFrames 3871, assign scores to our categorical data 3889, Mode 10574, Using the Regex A Za z we extract the initials from the Name It looks for strings which lie between A Z or a z and followed by a dot 12954, Detection of outliers 36964, that we have a proper model we can start evaluating it s performace in a more accurate way 20613, Checking for null values if any 10593, Partial Dependence based on gradient boosting regressor 39075, Models 6107, Most common categorial features 19642, Brands and models popularity 9081, There are a lot I next wondered if the order of the conditions mattered 26553, Reshape 3554, There is a 3 features that correlate with the Fare Survived Pclass and Family 35449, Confusion matrix 41221, No pattern for the elimintaed features the columns must have been shuffle 8126, EDA 6440, Missing Variable Treatment 35319, Data Augmentation 17464, Feature Engineering 10982, it s time to split the data we make 10 for test the the rest for the train 32987, Gradient boosting 21152, Alright let s fit GLM 42354, Firstly Applying lematization 20067, Processing date column into convenient format 19833, Logarithmic Transformation 35452, Few Word about dataset 3670, Encode categorial features can and should be replaced 16010, Submission 24663, Forecast preparation 41720, Define GAN architecture 31565, we now didn t convert numpy array 23997, Removing outliers Read other Kernels to understand how they were found out 7355, Concatenate train and test 40288, submission file single model on effnet b4 using bceloss 23308, Make predictions 32891, Make predictions on test set using the 1st level models predictions as features 9193, Cabin and Pclass Distribution 27200, In this section we randomly fill the missing data in our age variable with a value between the average of the age variable and the standard error 39761, Maybe it is interesting to calculate the punctuation ratio 1266, Identify the best performing model 13915, Sex and Embarked are categorical values but they are in non numeric format 21566, Convert a wide DF into a long one 14251, Most of the passengers boarded form S 7767, Ransac 37206, Stacking Averaged models Score 24731, Plotting Feature Importances 12655, Some Final Encoding 5277, Modelling and Feature Selection Pre Requisite 21749, Simple Ensemble Learning 4724, Basic Analysis with Pandas 11379, Building the base models 41350, Condition evaluation is not correlated with price as clear as quality but we still can say that there is a positive relation between condition and price 7251, Load data 18959, Display distribution of a continous variable for different groups 35575, Sales by Store 14473, a pie chart percentage of Categories of people travelling survived 12671, Replacing null values by mean of the vaues of column data 6646, Categorize by Age bins PClass and Sex 34956, sex and embarked are categorical 15062, Import modules 24050, Encoding ordinal categorical features 35069, From these two CNNs that with more feature maps yielded a higher test accuracy 16360, Using Bar Chart for Categorical Data 38213, Creating Keras model 8438, Alley Fence and Miscellaneous Feature Miss Values Treatment 42884, Map View 8492, Partial Dependence Plots 34075, Name Title 19170, Target encoding unused 22206, Evaluating Ridge model on dev data 4461, Correlation heatmap of all variables 3418, Married women are listed under their husband s full name with their actual first names now in MaidenName 3891, Skewness 5093, Clean data 3940, Removing Outliers 11534, It is evident that male passengers are far less likely to survive than females 18752, Performing Joins 32400, Submissions 34340, Missing Values and Outliers 8342, we implement our last regressor which is SVR 41041, Final Submission 21961, Lets check the structure of the new dataset p 36730, Download the prediction file 28483, Correlations 20802, Create YrBuiltAndRemod feature 11730, Scores 34416, Average word length in a tweet 33806, Domain Knowledge Features 34517, Previous Credit and Cash 42941, Logistic regession with the added features 13459, Based on my assessment of the missing values in the dataset I ll make the following changes to the data 20716, LandContour column 6146, Polynomial features 28496, Plot feature importance 30150, Divide predictors and outcomes And take logging outcomes to normalize 11476, PoolQC 30609, Based on these numbers the engineered features perform better than the control case 18683, The folders train and test contain the images 15911, Title Filling Missing Age 21770, The next columns with missing data I ll look at are features which are just a boolean indicator as to whether or not that product was owned that month 26533, SHAP 5350, Diplay multiple scorecard with bullet 31787, To submit pred test prediction and manually add real LB score in the next cell 34458, TX 3 23322, Mean encodings for item Mean encoding doesnt work for me 41646, Cleanup Column Names 21740, There are some redundant values which we remove later 36368, predict test data 24664, Convert predicted new cases to total cases 9396, Optimization algorithm 520, Seaborn heatmaps 42391, Plotting Helper Functions 5446, Make a 1 level Decision Tree 31864, we fit this top model to the bottleneck training data we created 4621, The other 7 statistical values can be used for detecting outliers 18309, lag features from months ago 31084, RANDOM FOREST 21321, Data Exploration 3377, Batch Predict on your Test Dataset 26900, Score for A8 15950 38699, Anatom General Challenge 18260, SHAP Model explainability 5390, this is indeed an improvement over mean median 28880, Encoder 42986, Word Clouds generated from duplicate pair question s text 30667, Drop all necessary columns 36828, And as always we need actually do the training so we call the fit method on our data 28587, Bedroom 4549, Removing variables have greater than 70 of missing values 41777, Show intermediate output values 2907, The cabin feature is full of missing values 75 so I remove it as it is difficult to guess which cabins used certain people 22779, divide the states based on 5 regions namely 13758, Higher proportion of young children survived More survived than died 8779, Title 34093, There are also outliers in latitude longitude that need to be removed for plotting 14859, We are now ready to make our predictions by simply calling the predict method on the test data 18316, check if encoding item category is beneficial 3985, Categoricals Numerics columns 20582, Filling missing values column by column 9660, Splitting data into train and test again 7545, Linear Classifier 3720, Feature Engineering by OneHotEncoding 28657, Land 8102, PassengerId 37368, Feature engineering 9738, Dataset Checking 28738, X train data 12296, Fare 14402, Feature Fare 11070, Normalise 16228, Spark ML Models 14185, Plotting Survived distributions 18472, A closer look at the Store Dataset 2909, Fill the Fare Feature in the test set with median 34749, Preparing data 35477, Otsu s Binarization 32922, Generate model predictions 4482, Linear Discriminant Analysis 12431, Using pandas string operations 32647, Functions were developed to assist with graphical analysis of specific dataset elements and with cross validation scoring of regression strategies 31420, without logging each step set steps 1000 to train the model longer but in a reasonable time to run this example 1525, Some Observations 15877, Or the best score 41293, Comparing the values under each category label 17609, Random Forest 35915, Train the model 25963, of Order containing no Reordered product 22215, Augmentations 19399, Data Visualisation 29135, Target variable inspection 11323, PClass 15841, Age 22093, Create the Model 14965, Out of the total passengers travelling in titanic only could survive which is not even the half of the passengers 6062, Fireplaces and Garage 37981, Difference between fit and fit generator 18757, Segmentation of Lungs 25782, This feature is to hard to find is it usefull for use or not do some oprations and find the meaningfull data 12500, Untuned XGBoost Trained with Cross Validation 15470, Data Cleaning Conversion of Categorical Data to Numerical Data 30639, Family variable enineering 35791, Ensemble prediction 32933, Make a submission 40721, Training Performance 42960, I noticed that there are outliers in the Fare variable 22084, Visualization of our Data Sample Images 34797, Random Forest 37414, Test Full Dataset 12958, Filling missing values in Fare variable 3959, High Skewed Features 15294, Random Forest Model 38315, Code I used for word conversion you need to install googletrans package in your kernel 11347, Model Building 36476, Construction of private test set 1788, If you want to know more about why we are splitting dataset s into train and test please check out this 27312, Model Compilation 28792, Positive Tweets 406, XGB 27520, Probabilities of predicted vs actuals value 24892, K Nearest Neighbors 39969, Passengers with the most expensive ticket survived 16156, Cross Validation K fold 10889, There are 2 features in our dataset SibSp gives the information about the sibling or spouse of the passenger onboard and Parch gives information about the parents and children of the passenger onboard But both these variables basically indicate the family information of passenger onboard we combine these 2 variables into one variable family 25595, Forming a Classifier 38645, Embarked 38718, In this step we combine the generator and the discriminator 34007, No outliers 3782, GradientBoostingClassifier 13501, Fare 7385, Inspecting the unmatched passengers 6604, Modelling 5931, Some of the non numeric predictors are stored as numbers convert them into strings 24060, We ll use Tree structured Parzen Estimater which is a form of Baian Optimization 23409, In practice we can t use the identity function as f everywhere because of inconsistent dimensions so when the input and output of the H l have the different dimensionality we use the 2d convolution with 1x1 kernel as the f function 3790, Statistics in case of positive skewness log transformations works well 24536, Number of products by Customer relation type at the beginning of the month 24303, because we are using the categorical crossentropy loss method we need to convert y train y test using one hot encoder 7072, Since Fare is mainly related to Pclass we should check which class this person belongs to 9028, I found the standard deviation of the BsmtFinSF2 in houses with basements 37565, have a look at the data type of all the variables present in the dataset 2548, If you are single and male the chances of survival would be low as historical account of the incident women and children were the first one to be rescued hence we create a column solo traveller 20077, Insights 5492, check TotalBsmtSF mean and update the same to missing value 18699, let s use the plot top losses method to examine images which have the biggest losses along with 3834, histogram and KDA plots 707, Test 32767, After Data Augmentation 27077, Preprocessing 32021, We should also convert Embarked column to numerical using one hot encoding First let s look if there are any null values 15099, The first thing I want to do is parse out the Name column and extract the title s of each person s name so we can group the data according to a person s title This allow us to more accurately estimate other features in the next few steps Technically this is more of a feature engineering step but it help out in the data wrangling process so we include it here first 8084, Get cross validation scores for each model 42993, Converting strings to numerics 4717, we ll need to convert categorical features to dummy variables using pandas Otherwise our machine learning algorithm won t be able to directly take in those features as inputs 19950, Women and children first 14432, go to top of section engr 32506, Defining the Model architecture 16455, Highly right skewed 42109, Confusion Matrix for better understanding of True positive and Negative 4273, The properties with None MasVnrType have 0 MasVnrArea 20308, I haven plotted several pair of components looking for the one suitable in my opinion for a KMeans Clustering 21067, Removing OOV 18228, Baseline Models 32547, Having a skewed target affect the overall performance of our machine learning model thus one way to alleviate be to using log transformation on skewed target in our case the SalePrice to reduce the skewness of the distribution 32105, How to reverse the rows of a 2D array 6795, SVM 12452, it is time to deal with MasVnrArea 13993, Number of missing values 29985, The code in this kernel is almost entirely from the reference kernel 6087, We handle quantitative and qualitative features seperately 9944, A feature which categorizes the fare rates of the person 22434, Scatter plot 9990, Missing data 41782, Split Train Test 39348, PCA using Scikit Learn 13828, Making and Printing our predictions 15414, Intuitively one would expect a similar trend to that of passenger class when looking at ticket prices 33441, LinearSVC 6302, that we have prepared the chi squared and extra trees reduced datasets for estimators without the feature importances attribute we are ready to start modeling the selected algorithms 5553, Check Submission 4344, Data Analysis for one by one feature 29446, As we saw here the distribution of training and testing set are very alike and we can assume that indeed these two are from one dataset 8085, Fit the models 19919, Look at coefficients 34729, The run function is the main function 41601, Makes predictions 16151, fill out missing embark with S embark 38693, Probability of melanoma with respect to Number of Image per Patient 8728, Sale Price and Year Built 3659, Looks like svc clf and gb clf are performing best with sgd clf forest clf and extra clf close behind it 20725, RoofStyle column 3928, KNN Classifier 2315, Method 1b Quick and Dirty way to get 100 numerical matrix swap categories to numbers 42275, month 11298, Baian Hyperparameter Optimization This code is more of an example as it can take a long time to work with the gradient boosting models 13545, start exploring Age Column 21607, Use header and skiprows to get rid of bad data or empty rows while importing 32199, Cleaning Item Data 38026, Bonus what are insincere topics where the network strongly believes correctly 16440, Its high Thus any value derived statistically based on only Age column can mislead the dataset for the classifier 18111, A training and testing dataset are provided 32145, How to find the duplicate records in a numpy array 40085, One hot encoding 21232, Explore our data 3518, Visualising missing values for a sample of 250 37165, For fastai we can do something known as 1 cycle learning 35062, look at an example of data augmentation 37724, Removing low variance features 40091, Neural Networks 2874, Treatment 16884, New Features FamilyType Alone Small family Big family 42290, Save OOF Preds 26287, compute cost to compute the value of the cost JJ 29318, You are more likly to survive if you are travels with 1 to 3 people and if you have 0 or more than three you have a less chance 31551, since more than 50 values are missing so replacing with NA 29333, Linear Regression 3807, Random Forest 26638, Prepare the submission 29034, Ditch unnecessary features 38735, Since there are many titles with very few counts we map them to main categories 3814, First we load a distribution of the data 16046, Lets check missing data 4172, Equal frequency discretisation with pandas qcut function 18086, I would like to plot images with different dominant colors 14300, let understand about data 11047, Originally existing features 21617, Useful parameters when using pd read csv 11248, method from house price predictions notebookEnhanced House Price Predictions again 24757, Machine Learning 31748, Hair Removal 12923, As expected females have higher probability of survival 74 20929, We use the categorical cross entropy loss for training the model 12747, Title 36002, Plot Heatmap of correlation matrix 14141, Decision Tree 21605, Creating running totals with cumsum function 13365, Types of Variables 32525, Compiling the Model 35948, ExtraTree 21357, Plotting loss and accuracy for the model 6318, Linear Support Vector Machine 19818, Backward Difference 33093, ElasticNet regressor 42994, Random train test split 70 30 33084, Changing numericals to categoricals 39141, Predict test s labels 16773, TESTING FINAL MODEL 28389, Test different parameter values 2466, Backward Elimination 42614, we ll split the data into a training set and a cross validation set 885, Logistic Regression 9391, Dealing with skew values is very important for some ML models such that NN or Linear Regression that are bases on normal distribution hypothesis 9072, much better The data points are now fairly symmetrical and there isn t as many outliers on one particular tail 38094, Here we are visulizing the 20 random test images 43089, check the distribution of these new engineered features 23522, The 85 mislabelled tweets 43112, For large datasets with many rows one hot encoding can greatly expand the size of the dataset 33711, FEATURE Pclass 41624, begin by looking at the summary 15617, Lone Travellers Feature 1405, If it comes to Cabin variable I m gonna fill up NaN values with Unknown and get first letter from every Cabin in dataset 10133, XGBoost Extreme Gradient Boosting 31577, There are FAR less ones than zeros 32008, We don t have a serious imbalance in our dataset The distribution is approximately to 11261, Importing the dataset 7079, We ll create a function to filter the girls 8741, Missing Values 20224, submit 11976, we fill the year of bulit of garage corresponding to the median built age of home and the Lot frontage with 68 33665, Current Date Time TZ font 4080, Correlation matrix heatmap style 3486, The parameters of the model with that score 42032, filtering small categories using nlargest 24283, Summary of most important features 8149, Linear Regression Model 32265, Relationship between variables with respective to time with different dot size and no dots 14720, Visualizing this Info 17575, Prediction 9435, Regplot 7235, Lasso Regression 36612, Visulaize a single digit with an array 1425, Random Forest Classifier 16508, Data Cleaning 34855, Undersampled 8811, EXPLORATORY DATA ANALYSIS 35901, Great we have to convert our predictions to a proper format 12390, Training set 875, fourth model model 3 Age bin SibSp Parch and Fare bin 12031, Below we re doing the preprocessing stages I didn t give any details about these if you need please go to 35600, Simulation 24278, Model summary 38820, we setup Boruta 37489, Simple RNN 9791, I had hard time to find a solution to encode nominal values with missing data 34102, Containment Zones 1579, Our last helper function drops any columns that haven t already been dropped 77, Here in both training set and test set the average fare closest to 80 are in the Embarked values where pclass is 1 2258, Pclass 24792, SWA 3655, Separating features and target 17912, Having more than 5 siblings spouses was very rare 39758, visualize our meta features 42038, Groupby Crosstab 38487, Evaluation div 30275, Some more analysis On Testing 18431, Random Forest 15990, Decision Tree 41192, There are many ways to fill missing values in numerical columns 26889, Include numerical columns Label Encoding 37018, So some categories are expensive but most are cheap 30325, Convert start end position into selected texts 24550, let s plot which products were chosen as the only product in case of the total products is one in any single month 31103, Observation of First 5 Variables with SalePrice 8384, I start exploring the categorical object variables 9219, Visualize the Best Model Fit for KNN 7013, Slope of property 13072, XGBClassifier 10595, Piplelines with Gradient boosting 30927, Sort the items in decreasing order of error amount 985, Import Whole Regression 17838, Model with Title FamilySize Pclass features 42070, Using sklearn to try using standrad scaler 11726, AdaBoost classifier 1001, To do this we must use Regular Expressions e sequences of characters which define a search pattern 16661, Categorical Features 11998, calculating R squared value for ridge 28283, For local use cache processed texts 25835, Calculating and analyzing No of words in each text 24937, Another fairly popular option is MinMax Scaling which brings all the points within a predetermined interval 25577, GarageArea 6766, Random Forest Regressor 37697, Looks like numbers 23950, Label Encoding the categorical features 15259, Removing unnecessary variables 31649, Model 5 GRU Add 29733, it s time to call the function from Baian optimization framework to maximize 16063, Feature engineering 20068, Calculating the amount of sales per a day 13418, Gradient Boost Parameters tuning 33760, Extract Dataset 14896, Parch and SibSp vs Age 40247, Sale Price 3452, The majority of 3rd class passengers would have been in steerage with no cabin designation 33289, Fare Imputer 10314, Create the ensemble 1366, To try a new approach in the data I start the data analysis by the Name column 16688, look at the relation between Age and Survival 20460, Income type of client 30087, Decision Tree Algorithm 19000, Deal with category imbalance 8557, We can use for slicing the dataframe 30348, Add active column 19550, Preparing Submission CSV 29625, Title feature review some interesting insight 2251, Data Exploration Analysis 12379, Since non numerical categorical data in a dataset be displayed in an alphabetical order in the graphs we need to provide a dictionary with orders in order to override the default order 21956, creating some plots 40478, Logistic Regression 11678, Your first machine Learning Model 14687, Lets try Neural Networks to get a better classifier 16003, Titles 1348, Completing a categorical feature 19648, Benchmark predict gender from phone brand 3599, Ensemble 37542, The major difference in the code comes here 32728, XGBoost part 35605, Errors 34964, Separate the Title of the Individual 32613, Exploring the Target Column 6864, Dealing with 3SsnPorch ScreenPorch EnclosedPorch OpenPorchSF which is the Area outside the House 16912, Feature Selection 41364, There is a correlation 15146, Seperate dataset to train test set 35495, Normalization Reshape and Label Encoding 3837, crosstabs 28703, take a quick look at what these new datasets look like 4603, Miscellaneous 42967, Splitting the Training Data 15937, Age 30905, examine the zooning code used by three different county in our data 14666, Train and Validation Split 14924, Building Machine Learning Models 28586, BsmtHalfBath BsmtFullBath HalfBath FullBath 35093, Creating a submission CSV file 29756, The dimmension of the processed train validation and test set are as following 32527, Loading the weights 18065, LinearSVC Model 22603, Number of days in a month 19822, Define a binning function for continuous independent variables 37145, PREPROCESSING STEPS 40396, Fold 3 25582, LandContour 42330, Information of ImageDataGenerator can be obtained from here class 33700, Custom Range Date font 25175, Analysing our extracted features 13825, Calculating Accuracy Score TP TN float TP TN FP FN 19803, Replacement by Arbitrary Value 19907, Month 1917, Alley 20593, Loading Datasets 4804, now split out train set further in to train and test sets for validation 19566, In each row we have image name width and height of the image bbox coordinates and the source 21069, preprocessing Training Data 3469, L1 regularization can be used for variable selection as it shrinks the coefficients of some variables to 0 19365, Show scatter plots for each numerical attribute and correlation value 24677, CNN Architecture 27296, fit SEIR model on country 31736, Image shapes 9311, Here it is easy to argue that Ex Gd TA Fa because Excellent is better than and so on 13069, Support Vector Machine 11557, first start by reducing the number of the dataset by applying a feature selection pipeline 27990, SHAP Values 22181, ALBERT span 32532, Compiling the Model 34294, Evaluate Convnet 13426, Function to perform Grid Search with Cross Validation 20707, Submission 25580, Alley 18581, PATH is the path to your data if you use the recommended setup approaches from the lesson you won t need to change this 23641, RNN 20811, Here we create a num transformer and a cat transformer for imputing and hot encoding numerical and categorical values 27574, ps ind 15 7623, loss ls lad huber quantile optional default ls 18407, After cleaning the tweets glove embeddings and fasttext embeddings are deleted and garbage collected because they consume too much memory 695, plot the Precision Recall curve and find a point with a better precsion 9398, Optimization 592, Together with the PassengerId which is just a running index and the indication whether this passenger survived or not we have the following information for each person 22922, Here it gets a little more complicated as name comes in string data which we must mine the data from 20109, Item count mean by month sub item category shop for 1 lag 24743, Target Variable 26923, And a little bit more of the linguistic tools We use a tokenization and a stop word dictionary for English 8219, Removing Outliers 15926, Age 10667, Plot the model 14814, Pclass Survived 15369, Sex Feature 3656, The basic approach for predictive modeling is as follows 2900, RandomForest Regressor 12946, Feature Scaling 37148, DATA AUGMENTATION 5531, Family Size Feature 18208, Post Process font 27834, One hot encoding of label 15943, Sex 4420, Using random forest 3521, The Challenges of Your Data 22197, Here are some unused RMSE and RMSLE functions 37095, Inconsistent types 1965, Model 38225, Category 28104, Pair Plot for all cols 24567, Total number of products by income 18026, NaN by feature 1230, Optional Box plot 7081, Exploratory Visualization 3797, According to the data description value NA means None for these categorical features Fiil NA with None for them 31113, Missing values 32329, Additional features 32466, Rate of Spread Model 3182, Random Forest Gpu Rapids 8736, Apply Log Transformation 24881, There was an empty Fare value in test 31606, Separate Features and Labels 14173, Grid search is used to tune hyperparameters to improve model performance 30281, India Data 1128, Exploration of Fare 23487, Creating a Baseline Model using TFIDF 38461, Output 26478, Augmentation 41122, Orange County Average Absolute Log error 14791, Is Alone 40315, Explore keywords 242, Library and Data 241, Model and Accuracy 13179, First of all let s take a look into our numerical variables correlation 34164, This plot says what we already figured out from the previous barplot 26427, In the previous analysis we found that that family mebers of small families have a higher survival chance than singles or passengers with a big family 12162, Max Depth 12741, first take a look at the train dataset and then learn the correlations between Survived column and other columns 1777, Model Accuracy Score for Logistic Regression by using Train Test Split 12277, Start the code drop some outliers The outliers were detected by package statsmodel in python skip details here 41860, Apparently tweets containing keywords derailment wreckage and debris are assosiated with true tweets 3164, Back to the main program we now apply the one hot encoding 13347, Random search allowed us to narrow down the range for each hyperparameter that we know where to concentrate our search we can explicitly specify every combination of settings to try We do this with GridSearchCV a method that instead of sampling randomly from a distribution evaluates all combinations we define To use Grid Search we make another grid based on the best values provided by random search 7682, We can safely remove those points 32805, Generate L1 output dataframe 31217, Exploring Categorical Columns 39175, index 27477 4841, NOTE if we do not labelENCODE numerical variables BEFORE we apply dummy encoding than these variables never be encoded Since dummy encoding works only on categorical variables 21411, Run model on validation data and save output 24600, Generate test predictions 17393, Sex wise Survival probability 39011, Keras works with batches of images 5054, Interesting but hard to decipher bin the data to decades 9134, Set Fence Quality to 0 if there is no fence 8440, Final Check and Filling Nulls 2872, Identification 21119, As we want to apply the same data cleaning and preprocessing then we temporarily connect them 31728, Diagnosis and Target 36349, Predictions 13717, ALL MISSING VALUES HAVE BEEN HANDLED 36144, Reshape 37691, How to make predictions for the whole dataset use matrix multiplication again 23387, I experimented with a few optimisers including FTRL and SGD but in the end Adam was the fastest and most reliable function 35479, Scale Up Scale Down 1576, We fill the null values in the Embarked column with the most commonly occuring value which is S 29013, Distribution of Sex feature by Survived 831, columns and correlation after dropping 7237, Final Prediction 21212, There are no missing values in training and i also checked in test data also 35554, Let s Vote 27347, Arima AutoRegressive Integrated Moving Average 19255, Daily sales by item 12039, I m putting 0 in GarageArea GarageFinish GarageType GarageYrBlt and GarageCars where houses don t have garage 21892, Plotting same metrics for each item category 21938, Spark 5457, lets calc the CI s for the rest of the samples and Collect in a table 26506, To prevent overfitting we apply dropout before the readout layer 41163, FEATURE 9 OVERDUE OVER DEBT RATIO 39841, Visualizing the data 32632, to evaluate a default configuration 24872, Creating neural networks is pretty simple in tensorflow 24700, freeze the first layers for the first epochs 16117, Linear SVM 35550, Stacking 9025, Great Since there are only 2 and 3 missing values in these columns respectively and their values have the same range I just set the null values to the value of the other 17877, Embarked 23547, Training the dataset 10823, Excellent 18654, Read the data 3989, Check data again 18632, Exploring Dates 4734, Estimate Skewness and Kurtosis 11121, List of selected Categorical Features 37461, Stemming operations 19426, And as before we add new methods to our LightningTwitterModel class 519, Exploratory Data Analysis 34326, Since we re gonna use two different types of multi input model with flow from directory I m using two generator 21257, Set Ratings Dictionary 27042, Age Distribution of patients 3466, Split into input and output dataframes and define a variable to hold the names of the features 16765, Mapping and removal of features 27321, Plotting model accuracy 27212, Preparing the Datasets 41559, Stage 3 Understanding and Applying PCA 43282, Avalia a m trica das previs es geradas no conjunto de treino 6667, Decision Tree Classification 27395, I get the best paramaters using Randomgridsearch with 4 folds 19970, That fits our intuition from the other charts then 33569, Process some features with LabelEncoder 42546, Animation over average number of words 4645, Violin Plot 26407, The survival chance of a child with 2 or less siblings is much higher than than for a child with 3 or more siblings 9215, Gaussian Distrbution for Fare 22107, Write TFRecords Test 37759, Technique 7 Eliminate unneccessary loops by using itertools 10781, Remaining columns 42949, Sex Feature 22748, Crimes by year 2003 2015 21544, our CNN model consist of a convolution followed by a max pooling 36782, Classification 37749, Suppose we want to fetch a random sample of 10000 lines out of the total dataset 6119, Veneer area 18327, Try Ensamble 26516, Age distribution 36870, Keras only input and output layer 1902, Evaluating Model Performances 28561, OverallQual 8981, Given our new features we can drop Condition1 and Condition2 11949, Preparing the test and train dataset span 16064, Predictive Modeling 3398, MasVnrArea and MasVnrType NA most likely means no masonry veneer for these houses 30897, plot the location before we fill the missing value 1963, Correlation Between The Features 28685, Although this feature is a numeric feature it should really be a category 36210, Evaluating Ensemble Models 32176, FEATURE ENGINEERING 14286, Classification report analysis 27418, Less learning rate 38050, Chi Squared Goodness of fit Test 1193, To prevent overfitting I ll also remove columns that have more than 97 1 or 0 after doing pd 809, The target variable Distribution of SalePrice 30618, We save the statistics dataframe in a variable as we can use it at a later time 14821, Age is nor correlated with sex but it is correlated with parch sibsp and pclass 41217, The AUC on public LB is when using only variables a very slight drop from using features 24058, SHAP importance 28055, count acts differently in Pandas and Spark 6552, Survived The first class people more likely survived 17622, Fare Cleaning Fill missing Fare value by taking median of Fare for respective Pclass As Fare is proportional to Pclass 17897, Encoding variables 24585, Run Training and Validation Step 14615, Station 2 Scaling the age 29555, Method for wrapping TabularDataset into iterator 35205, Increasing worsens the performance of Lasso 38319, Break the model 40974, Daily Revenue summed up into Monthly Reevenue for every store using 19558, coco transforms 41349, House prices are normally correlated with overall evaluation 40270, Exterior Quality vs Sale Price 6566, Cabin 2829, get only the column used on the training set to predict on the test set 12685, Visual Data Exploration 20928, We choose a 4 layered network consisting of 2 convolutional layers with weights and biases w1 b1 and w2 b2 followed by a fully connected hidden layer w3 b3 with HIDDEN hidden neurons and an output layer w4 b4 with 10 output nodes one hot encoding 7586, Like for GrlivArea there are two outliers at the lower right also for all SF 41713, Lung segmentation 36510, Embarked Sex Fare Survived 7811, Interpret LightGBM Model 15054, Categorical variables which classified to less categories could improve the correlation 32235, launch our session In TensorFlow you always launch a session to run computations and assign placeholder values 14464, back to Evaluate the Model model eval 20378, Write predictions to submit it 35087, Predicting values on training set 14846, Since we don t expect that a passenger s boarding point could change the chance of surviving we guess this is probably due to the higher proportion of first and second class passengers for those who came from Cherbourg rather than Queenstown and Southampton 11823, SalePrice Correlation Matrix using HeatMap 30463, Visualizing the most informative features 7442, Combining the two datasets and then doing One Hot Encoding on the combined dataset 15854, Namelength 35868, Splitting in training and validation set 7223, These houses do not have any fireplaces and FireplaceQu can be replaced with none 4644, Box Plot 14519, Age 30423, Load model 39834, Main part load train pred and blend 24818, predicting model and submit solution 16570, Voting Ensemble 19941, Embarked 32122, How to drop rows that contain a missing value from a numpy array 33145, Training the Map 17677, Now I m ready to predict test data set with xgboost algorithm 8023, We found few missing values in few of Columns 18219, Train 15 CNNs 8451, Drop the features with highest correlations to other Features 36513, 1st class passengers are older than 2nd and 2nd is older than 3rd class 1687, Correlation Heat Map CorrelationHeatMap 20957, Overfitting and Regularization 41975, To read bottom 5 lines 18761, After filtering there are still lot of noise because of blood vessels 42140, Decoder 13599, we can fit and transform our data 19673, Align Train and Test Sets 11638, Gradient Boost 26964, which shop is the highest and lowest revenue 22799, look in detail the categorical features 35524, The last part of Feature Engineering is box cox transformation 16187, drop Parch SibSp and FamilySize features in favor of IsAlone 4081, SalePrice correlation matrix zoomed heatmap style 1218, Turn Nan to None 27075, we generate a histogram of headline word lengths and use part of speech tagging to understand the types of words used across the corpus This requires first converting all headline strings to TextBlobs and calling the pos tags method on each yielding a list of tagged words for each headline 24100, Dropping the rows which contains outlier values 26967, which item category is the most popular and the worst 18561, Family 6728, MoSold Vs SalePrice 24163, Training Samples 15069, Survived 35150, Plot the model s performance 8956, No NULL value is remaining 5046, To examine the relevant correlations more thoroughly we plot SalePrice vs OverallQual The latter is a categorical ranging from Very poor to Very Excellent Rather than a scatter plot we use box plots for categoricals 10650, Sex Pclass and Embarked 17686, FILL THE MISSING VALUES IN THE DATA 28713, Joining the tables to our train dataset 4689, Do you remember you do 29474, Distribution of the fuzz ratio 41606, plot several images with their predictions 32468, Fatality Rate Model 40483, Random Forest Regressor 4954, we ll start with LASSO Regression 29996, XG Boost 26989, Predict 22117, Some point are far from the red line 41709, Resampling 40154, We have few variables with missing values that we need to deal with 15014, Embarkation Location 26362, With the nvis object created let s make use of the NetworkVisualiser 4226, Mean Encoding 4542, random forest decision tree decision tree 4304, Inference 28238, Changed epoch from 1 to 10 5550, Fit Model 37064, Imputing Missing Variables 19447, The accuracy as measured by the different learning rates and are around and respectively As there are no considerable gains by changing the learning rates we stick with the default learning rate of 13868, Dropping UnNeeded Features 31790, Prepare tools of the original kernel 12509, Transformations 16593, Importing RandomForestClassifier 33683, Add Days font 17867, Submission ensamble 5061, let s have a look at sale types and conditions 3838, Pivot Tables 21557, Name 9104, This distribution looks skewed to the right 41340, Numerical Features 19604, Columns with missing values 9902, Drop Passenger Id and Cabin 39184, Percentual de incidentes por endere o 14691, Naturally the wealthier passengers in the higher classes tend to be older 40628, Yep looks ok 35370, Create train and valid datasets 18243, Removing the Outliers 30752, Fixing max depth and min samples split 37894, Random Forest Regressor 23021, One Item Features Analysis 23934, Looks like the target distribution is more concentrated between but there are still values until 13890, There are 8 NaN tickets because they didn t have a number in them 18820, Gradient Boosting Regression 35871, Confusion Matrix 2884, In my perspective heatmap is the best way to have a look at different correlation without going through all the troubles 38414, SGDClassifier 32330, Important Features selection 11844, Bath 14376, MOst of the passengers were of Age 17 to 40 31568, we can look at images and labels of batch size by extracting data 25281, Display images 13829, we save the prediction in the following file 6866, Dealing with With FullBath HalfBath 42094, Importing Data 12625, Fill the missing values 41935, We use the TanH activation function for the output layer of the generator 7659, stack averaged models with a meta model 14428, Create function replace titles to update the titles verifying Sex of some of the titles before replacement 19927, We detect 10 outliers 31639, Dates minute Encoding 30681, NN is the future 8130, Missing values 37630, In the example below I have used different arguments for the shift scale and rotate limits to make it easier to visualize what happens if we do not specify the border mode and value arguments 1615, Model 1543, Fare Feature 6763, OneHotEncoding 19640, Any models that can belong to different brands 6800, XGBoost 32114, How to convert a 1d array of tuples to a 2d numpy array 20075, Insights 125, Modeling the Data 1381, Preprocessing 7482, I decided to divide these into 5 categories Miss Mrs Mr Noble Crew 8831, Balancing Dataset 7008, Pool area in square feet 9074, I wonder if Exterior1st and Exterior2nd are ever equal 27238, Trend by Country Region for the maximum cases 13470, Exploration of Gender Variable 36825, Here we re simply converting the features to an array for easier use 17961, Converting to Markdown 19730, Observation 6730, SaleCondition Vs SalePrice 12150, If we have a larger dataset we can e 25209, Analyze the GrLivArea Above grade ground living area square feet Second highest correlation with SalePrice 15473, Feature Fare 21164, Normalizing data 4510, Observations 34295, Visualize Activations Benign 17532, Complete Fare property and create custom Fare bands 16295, Describing training dataset 38980, test data 6742, CentralAir Vs SalePrice 36868, Multi Layer Perceptron 16232, it s time to call pipeline MLlib standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline or workflow we add all the commands which we have called till now and add them to pipeline 33042, The default boosted model produces around 84 accuracy in the prediction which isn t very good 9599, Finding out the Family Size 36106, Binary LSTM itself 1514, now lets remove these 0 values and repeat the process of finding correlated values 1998, Well the most correlated feature to Sale Price is 6830, Categorical Features 27332, Checking for NULL values 19708, The two variables train data and test data be used for storing the modified prep data generated from train images and test images 7230, Log Transform the skewed features 15686, Checking missing values in more detail 13525, Remember All transformations that were done in the training dataset must be done in the test set 3888, Median 3813, Submission 32604, Bayesian Optimization on the Full Dataset 16559, Basically the columns SibSp and Parch tell us whether the corresponding person was accompanied by anyone or not we create a new column Is alone which tell us whether the person was accompanied 1 or not 0 36371, Creating a CNN Model 39281, Feature analysis 16476, SEX 37220, Choose Embedding 20065, Normalizing category columns which have similar values 1557, Additionally looking at the relationship between the length of a name and survival rate appears to indicate that there is indeed a clear relationship 8372, Logistic Regression 21761, Some entries don t have the date they joined the company 38705, After Mean Encoding 33642, Missing Values 16907, fillna 35412, Attribute Bathrooms Bedrooms 8296, Random Forest 4366, 1stFlrSF First Floor square feet 6858, Dropping Columns 20709, Removing outlier 4939, Handling numerical missing data 24116, NN 15608, Embarked Feature 18242, Predictions for testing data 24404, check the distribution of the standard deviation of values per columns in the train and test datasets 32578, Example of Sampling from the Domain 34280, check for outliers 9654, Filling other missing categorical and numerical data with None and 0 36739, Reading Test CSV Data to Predict values 16624, Cross Validation 16266, Sex 42398, How do sales differ by store 1519, look at their distribution 1304, Observations 2389, RandomizedGridSearch 15296, K nearest Neighbors 32190, We can print some of images from the test set and print the corresponding predicttions to get a sense of how good our model really wasWe can print some of images from the test set and print the corresponding predicttions to get a sense of how good our model really was 7566, describe for numerical features 19735, For each Department 16694, generate classes for different titles and divide them into different title classes 41233, Reshaping the data to the format Convolution layer expects 33996, Recurrent Neural Network RNN 23838, Taking X314 and X315 24442, Load Data 17046, Random Forest 41490, Evaluate Model Accuracy 38083, We normalize the train data and test data and we do this by dividing the data by 255 23535, Load data 37137, Multiclass Classification 5431, I would assume that the 690 just don t have a fireplace 19010, Predict on the submission test sample 9313, To give a simple ordering to these categories we can do the following 17851, predict with the validation data 5534, Create SibSp Categories 18394, To select the best model out of the five we define a function score model below This function returns the mean absolute error MAE from the validation set Recall that the best model obtain the lowest MAE To review mean absolute error look here validation 31360, Pretty Printing 32086, Table 2 The 5 features that have the highest absolute correlation with log 24480, Strategy 2 Add data augmentation and a learning rate annealer 18005, Here Sex 1 implies male and Sex 0 implies female 43042, This is the fifth part of the algorithm mentioned below 11628, my baseline model be LogisticRegression 5710, Exterior1st and Exterior2nd Again Both Exterior 1 2 have only one missing value We just substitute in the most common string 31377, Translate image 23182, Feature Importance 1004, So what would you do here The first thought is to extract the salutation do you agree RegEx it is once again 34711, Mean over all items 40256, Total Rooms Above Ground 14273, Interpreting Confusion Matrix 1759, Code for finding outliers individually 30996, Adding a Relationship 32537, Generating csv file for submission 13737, Confusion matrix and accuracy score 41788, Check the output button for the training description 40642, Preprocessing 2435, Nice The lasso performs even better so we ll just use this one to predict on the test set 13094, Frequency distribution Categorical Variables 17049, Optimal parameters found during cross validation are lower than on public leaderboard meaning we are overfitting to training set decrease max depth and decrease n estimators to make model more general 9868, Parch Survived 6402, Preprocessing Test File 16371, Checking Size of Family 31929, visualize random examples predictions from the test dataset 9669, we can say that we have a quite well clean dataset to provide to our classifier algorithm 19319, Make Predictions 15525, The names begin with certain titles Master Miss for children and Mr 838, StandardScaler 6218, BsmtQual Evaluates the height of the basement 27174, Feature Selection 37216, Much better 853, mean of best models 31832, Over sampling followed by under sampling 34250, The Basic idea of Time Series prediction and RNN is to re arrange the data 10867, Fitting the vectorizer 38991, jaccard score on train data 2734, Reading the two datasets that are going to be used to demonstrate various methods of handling missing values 512, Gradient Boosting 25512, PARAMETERS OF THE EMBEDDING LAYER 23564, Animation 13886, Passengers Fare 4127, Stacking Averaged models Score 19817, Contrast Encoders 8789, Training with the whole dataset 33853, Analysing extracted features 1224, Observe the correction 40983, Grouping by multiple columns 15676, Plotting Learning Curves 3929, MLP Classifier 11472, Confusion Matrix 23619, Gini coeficient 35778, Calculate Metrics for model list 8662, Instructions 18435, Predictions 9478, This function ensembles the trained base estimator using the method defined in method param 65, submission 21773, Based on that and the definitions of each variable We fill the empty strings either with the most common value or create an unknown category based on what I think makes more sense 43006, from pyspark sql functions import is need for the countDistinct 6441, More than 50 of data are missing for PoolQC MiscFeature Alley Fence 20829, we ll fill in missing values to avoid complications with NA s 23643, Augmentations 11023, People embarked at C port have better survival rate 235, Library and Data 12641, Name Feature 39409, Sex 19463, Plot the accuracy and loss metrics of the model 41590, MISCLASSIFIED IMAGES OF FLOWERS 282, Machine learning algorithms typically do not handle skewed features very well 9617, Feature Engineering 22189, The brand name data is sparse missing over 600 000 values 537, Looks like there is very strong correlation of Survival rate and Name length 21142, let s look at its moments 6104, Four features have more than 80 of missing values 2170, Sex is one of the most discussed topics in Human history 7212, Lets check which features are co related to our Target variable SalePrice 26729, Plotting monthly sales time series across different stores 18954, Display distribution of a continous variable for two or more groups with Mean and Standard Deviation 28354, EDA of Bureau Data 40993, While for example aggregate function reduced DF this function just transforms our DF 18601, Confusion matrix 38513, Again more or less word count is also similar across positive negative and neutral texts 42056, Dispalying pie chart and countplot together using matplotlib 40850, Usually we drop a variable if at least 40 of its values are missing 6868, Dealing with LowQualFinSF MiscVal 34953, Model accuracy assessment 3375, Select the Target in your dataset 18169, Data access 87, Age Feature 7111, We use logistic regression k nearest neighbors support vector machine Gradient Boosting Decision Tree as first level models and use random forest as second level model 42181, loss function is categorical crossentropy 31235, Features with max value more than 20 and min values less than 20 4113, Fill the columns with None or zero accordingly 24926, Images 28798, Predicting with the trained Model 13866, Mapping Feature 27214, Modelling 18492, I think it s always better when working with decision tree based models to have dummy variables instead of categorical with different levels because this alters the bias of the algorithm who favor a higher weight to the categories like 4 and deprioritize levels like 1 21777, Training 22612, Randomized Search 36750, Take last days 14 for this notebook in order to predict firts unknown day s sales 31413, Pack the test set as what we did for the training set 35884, Add time lagged features 6831, Numerical Features 21665, Prepare Dataset 7419, Remove additional variales and add missing variables in test data 1929, MSZoning 6893, And here is the rate of survival by class 15851, Cabin 32243, Convolutional Neural Network 22270, Age 20939, Augmentation 38206, Predict and Make Submission File 760, define the autoencoder model 26499, TensorFlow graph 5460, Which specific attributes lead to more uncertainty 4231, Data Manipulation 41202, check how our model behaves on the training data itself 29591, Defining the Model 4041, Categorical Numerical Variables 19183, And now we create our submission 34420, we move on to class 0 15705, We have created 8 age categories that follow the original distribution of Age values 12952, Numerical variable analysis 11502, The danger in label encoding is that your machine learning algorithm may learn to favor a over b 19808, look at another example feature like Embarkment 10730, Random forest classifier 3636, Extracting Title from the Name feature 8560, Replacing Values 26309, we need to handle missing data 7174, that we already convert this features let s take a look into their correlation with our target dooing a correlation matrix plot 6744, OpenPorchSF Vs SalePrice 20323, Section 4 3D Convolutional Neural Network 40268, Feature Selection with categorical features can be a bit tricky as there are many proven ways to do so but no standard process 34235, And now we can build our DataLoaders and you re done 12459, XGBoost Regression 25948, Feature Engineering 24049, Imputing nominal categorical features 23379, I did some more manual inspection of the bad labels picked out of this code that I have not included in this notebook 18891, the really last step is scaling and data transformation 41528, by eye it looks as if there may be some issues distinguishing between 5 and 6 on occasion 2006, Everything looks fine here 33890, previous application loading converting to numeric dropping 31627, Train and predict 32465, Reach Model 5250, Feature Importance Scores From Model and via RFE 16592, Define Feture importance function 37665, Data visualization 10374, Test data 14408, deleting Survived copy feature because I made it just for EDA 36092, And how successfully 33743, We have to map the image vectors back to image 41425, Missing values in the macro economic data 35347, Model Building 9837, K Nearest Neighbor 31109, finally we use cat col dataframe and good label cols for one hot encoding and later be used for prediction 28405, And KERAS 23323, Number of month from last sale of shop item Use info from past 35707, Compare the r squared values 23650, Submit to Kaggle 40624, we can construct the pipeline and transform the training data ready to train the model 39425, get rid of Cabin for now 12754, Get dummies 14004, Random Forest br 23711, Run the next code cell to get the MAE for this approach 22924, now we have a column of titles such as Mr Mrs and Miss 6659, Drop unnecessary columns in train and test set before predictions 32937, Feature Engineering 28476, New features based on area adapted from THIS KERNEL additional features scriptVersionId 1379783 41578, BREAKING IT DOWN 14074, AgeGroup 41596, To verify that the data is in the correct format and that you re ready to build and train the network let s display the first 25 images from the training set and display the class name below each image 42345, Preliminary model selection try five different regressors at first 3912, MasVnrArea and MasVnrType NA no masonry veneer for these houses 36616, Calculate unit vectors U1 V1 and new coordinates 3268, move to handle missing values 41808, Image augmentation 3695, Data Pre processing 19912, Split train test 39151, There are many different types of CNNs For now we only use one type of CNN ResNets The come in different sizes There is resnet18 resnet34 and a few more At the moment you just have to select the size 6059, Spaces and Rooms 32090, How to create a boolean array 7138, Fare 38272, As each word is just a sequence of characters and obviously we cannot work with sequence of characters Therefore we convert each word into an integer number and this integer number is unique as long as we don t exceed the vocabulary size It s not the one hot encoding It s basically just the transformation from a list of the words into a list of integer values 3767, Random Forest Classifier 22195, Define constants to use when define RNN model 32427, Training Function 8582, Applying Linear Regression 39169, To use a model pretrained on ImageNet pass pretrained True to the architecture like this 22470, Autocorrelation ACF and partial autocorrelation PACF plot 29708, final Inference and submission 3830, selection methods 27132, Discrete Numerical Analysis 8223, Working on integer related data 34447, Item Prices 13234, PassengerId string 6020, Submit 25978, Features columns 2430, Evaluation Our Models 20376, Build the final model with the best parameters 29321, Model 2 10156, Not effective because numbers are comparatively very large Apply log to avoid that 20766, Text Processing 27466, Most common stopwords 28215, the same in plots 13560, Fare by Age 22703, Visualize the Transformations 18096, We visualize a random image from every label to get a general sense of the distinctive features that seperate the classes 651, That s really cheap for a 1st class cabin 28270, We shall now take look at rest of the categorical features 31280, Naive approach 13910, Does age play a role 1265, Blend models and get predictions 10648, Cabin t is not much useful as we expected 31477, I trained the model on my local machine 34532, Putting it all Together 5194, Prediction 42102, Convutional Neural Network 39027, Tunning the hyperparameters 4815, Only numerical features left to feed our pipeline 41211, For accelation instead of calculating p xi y we now calculate p bin of xi y for every bins To achieve that we cut every continues value in xi into bins and map continues xi to its bins probability p bin of xi y This is bining Kernel Naive Bayes 41005, Resnet class 22182, Bart span 32771, Predict 15786, Confusion matrix 13757, Again clear that females have higher chance of survival 37090, Load our data 38774, Select model and predict test data 29151, Lot Frontage Fill with Median 23083, STEP 2 Features Engineering 6643, Survived and Non Survived male and female by PClass 33636, Descriptive statistics 10922, Displaying info on graph 21753, We use the same dataframe without further processing for Advanced Ensemble Learning so I save it to csv and use it in Part 2 12818, Embarked and Fare Analysis 34738, Last 3 dimensions of the Latent Semantic Space 4307, Training Dataset 12227, Read the data 5672, Check if there are any unexpected values 31305, Neural network with Keras 18893, eXtreme Gradient Boosting 9316, which one is better 31475, Model 16447, Age 19916, Here I am creating one lag for test data by just replacing subsequent column name 18132, SVR 13358, Types of Variables 25784, there are lots of unique values are here so take first character of Cabin 17544, Submission 8903, Stacking 16313, Plotting some distribution plots based on survival s sex 30183, After one hot encoding we have got 99 different species 10444, Not normally distributed so we shall apply logarithmic transformation 42547, Embedding with engineered features 21066, Removing Empty Documents 37889, Alpha 661, Nearest Neighbours 10637, We can extract some crucial information about passengers Age and Social Status from Title 20117, Fill null value with 0 for lag features 43303, Given the fact that they hold relativel small number of nulls I just set them to Unknown 21375, Keras expects the training targets to be 10 dimensional vectors since there are 10 nodes in our Softmax 9433, Point Plot 26940, The importance of the missing values 29978, Augment Images using ImgAug 23228, There is variance in the dataset so we scale the data 13581, Encoding 43009, Normalize Imbalanced data 8711, XGBoost 7582, new feature sum of all Living SF areas 2547, Imputation with median for numeric variables and mode for character variables 20638, Bigrams 28565, As the condition of the basement improves the SalePrice also increases 40855, Bivariate Analysis 15621, Cabin feature 37115, total training time is 65 minutes 28281, Needed for generating important absent vectors in embeddings 36299, Decision Tree 43368, Normalisation 15619, Family Size Feature 9760, Because I used handwriting dictionary to create TitleGroup feature there might be some titles which only exists in test set and be converted to NaN value 8572, Looping Over a Column 33102, Evaluation of the models 30942, Visualizing Interest Level Vs Hour 23878, Properties 2016 23319, Add previous shop item price as feature Lag feature 36423, Numerical Features 41183, print list of numerical columns 32774, Separate Train Test data 42121, To be continued 9652, Assigning None to missing categorical values 2293, Pandas whats the data row count 37, Embarked Column 31785, The most interesting model is LGBM with the first draft score 3762, Ridge Regression 40626, We are now ready to train the various classifiers 2182, One standard way to enrich our set of features is to generate polynomials 13891, To cleanup the data I am going to 20780, Reading Data 9708, Lasso 6427, Linear Regression Model 10119, Here it looks like passengers from Cherbourg have had the highest chance of survival when compared to Queenstown and Southamptom 31997, get the validation sample predictions and also get the best threshold for F1 score 22668, Most Common Words 20243, In EDA we decided to use family size feature 10558, Find all categorical data 12619, use prefixes in the name to Create a new column Title 8037, Insights 24828, Feature Selection 35094, documents meta csv 12129, Encoding some features features 1 34545, Here I am training the dataset in batches since the RAM cannot handle the entire dataset having 200 dimensional word vectors all at once I am training 30 000 samples in iteration A single epoch undergo training of all the samples in a dataset one after another batch 1 epoch have length dataset 30000 number of iterations 29621, Almost 70 of passenger are embarked in S 40684, Show Model Accuracy as function of number of clusters used 19330, Building a Sequential Model 32932, Train the production model 27105, Our dataset consists of 1460 rows and 81 columns 27205, 5th Step Predictions 5881, Most of them are right skewed 15135, Embarked 38232, Building the final model 41655, Distribution of data for numerical features 27749, Target variable visualization 20115, Number of days after first sale 37880, Residual Plot 5470, We do note that the columns have changed as well as the order That means some of the fields have absorbed some of the smaller importances into their score 5253, Permutable Feature Importance 5516, Decision Tree 4755, the value zero doesn t allow us to do log transformations 31396, We should scale the values in the data so that the neural network can train better 41316, Generate test predictions 34534, Remove Features 399, SVC 29216, Separate Train and Targets 36282, We can get the alphabets by running regular expression 40061, Submission 12398, Checking for null values 3178, SpeedUp Xgboost with GPU 37725, Feature correlation analysis 1324, Which features contain blank null or empty values 18764, After reading the 3D CT Scans we first segment the lungs and then generate the binary mask of nodule regions 38682, Min Max age of Patient 39137, We get the following output after applying softmax 30261, Confusion Matrix 3622, a Spearman correlation is conducted 36554, New feature importances 3990, Ooops 34848, EDA 19395, PCA transform the vectors into 2d 28753, Helper functions for image convertion and visualization 15400, There is one missing ticket fare value in test data 31252, Splitting Data Back into Train and Test 37479, In some applications it is essential to find out how similar two words or sentences are 32133, How to find the position of the first occurrence of a value greater than a given value 24144, Prediction for Test Data and Submission 2731, Writing submission files 16468, Split the data into train and test sets 27085, However in order to properly compare LDA with LSA we again take this topic matrix and project it into two dimensions 104, Squaring the correlation feature not only gives on positive correlations but also amplifies the relationships 19417, Before we start we can do some preliminary work 991, SHAP Values impact on model output 16509, first handle the missing values in our dataset 11285, Handling Missing Data 4840, Data Correlation 22644, Sales Mean 24748, A couple of features look severely depleted but the rest only suffer a few omissions which means imputing these blank variables certainly becomes an option 6096, We now begin an analysis on the normality of some of our very important features 2304, Best Practice Check and Convert the rest into categorical 16886, Small Family 33611, Fit our model to training features and corresponding labels 15447, visualize our new Deck feature 8844, The first thing we ll want to do is replace all missing values with some value 9017, If the there is no Garage just set GarageYrBlt to the average year that Garages are built 818, Outliers 21014, Matrix 1835, Remaining NaNs 39017, Segment the groups by age 31325, XGBOOST 9985, Skewness and kurtosis 7045, Type of dwelling 26801, Miss labeled data 8124, Bagging 37183, use stratified strategy 38307, Model building 7934, The pros of using Lasso and RandomForrest algorithms are to get insight on the coefficient weights and feature importances 4104, Set Artifial neural network and Learning 11449, Converting Fare from float to int64 using the astype function pandas provides 12609, check which features contain missing values 9626, Feature Selection Using Linear Regression 19291, Creating multilabels 8115, Guassian Naive Bayes 7312, Observation 40, Fare Column 31795, Below we construct another model using exactly the same layers 13127, Survival Percentage by Gender 23523, The list of all 309 strings after the cleaning 13107, K Nearest Neighbors 29188, Scatter plot of actual values vs predicted values HousingPrices train 41565, None of the kernel PCA give satisfactory seperation now let s try LDA 5102, we create a basic random forest classifier with 2000 estimators 1914, looks good to go 39044, These top 100 account for a good 20 of the whole training dataset 35331, Splitting into Training and Validation Sets 17821, plot feature importance 30866, Split training and validation data 33601, MaxPooling The function of pooling layer is downsizing the feature maps 1921, Fireplaces 16971, Interesting All the passengers with fares higher than 200 were in Pclass1 5028, LASSO Regression 983, Predictions 11324, Embarked 33080, Missing data 37209, Writing to submission 17973, Another helpful feature would be to check whether they were travelling alone or not 8576, right join or merge This joins return all the columns that are in right dataframe and the common columns in left dataframe 16952, PClass 12964, Age and Survived 6122, We just fill missing Zoning values with RL 17824, Here we plot the confusion matrix 3848, Feature Age 25168, Creating our very own stopword list after removing some stop words like how whom not etc that may be useful to differentiate between questions 6120, we replace missing Veneer area with O 27448, Categories 37174, Actual versus expected 11879, Predictions for submission 37141, Training Code 14202, Trying something a bit better 1157, Linear SVR 38502, Distribution of the Sentiment Column 12963, Pclass and Survived 21760, Missing Antiguedad min Antiguedad 8549, Masonry Features 8500, Create dtype lists 19336, Prediction Submission 22473, Multiple timeseries 20616, K nearest neighbor Classifier 24285, Import Libraries 11217, Find best cutoff and adjustment at high end 37451, we load the pretrained bert transformer layer 23919, Model on TFidf 11987, F statistic 6323, Extra Trees 17918, DATA TRAIN 2199, Gotta work a little bit in the Name column 16344, Comparing Models 33686, Days surpassed 11651, Extra Trees 12001, lasso also acts as a regularized model but unlike Ridge lasso not shrinks unimportant features but makes their cofficients zero so it also acts as a feature selection model 42179, Defining the model 32140, How to create groud ids based on a given categorical variable 32517, Model 4 New Model From scratch 32763, From 1D vectors to 28 28 1 4882, You can always use value counts to check on data visualization is just another option 24977, Train Predict and Save 17742, I wonder if the ticket number corresponds to port of embarkation 38815, WBF over TTA 13227, GridSearchCV for Random Forest Classifier 41445, we can reduce the dimension from 50 to 2 using t SNE 23828, Removing columns with zero ovariance 13567, Ploting Age Cat Distributions Fare 1175, As expected the lotfrontage averages differ a lot per neighborhood so let s impute with the median per neighborhood 12170, Reading Inputs 15695, Number of siblings spouses aboard the Titanic vs Survived 23902, tell BERT to ignore attention on padded inputs 4967, Family Name 21223, let s run the model for 3epochs it takes easily 30min 3027, Outlier removal is usually safe for outliers that are very visible in certain features 10627, we can drop and code Parch code 13846, before the cleaning data we combine training and test data in order to remain keep the same structure 40384, To understand what the function decode image does we use a sample filename to test it out 22405, tipodom Addres type 34958, Looks like 5 is the best number of clusters based on inertia 15430, let s scale the Fare and Age values to be in similar range to the rest of features 13841, Correlating numerical and ordinal features 38097, Feature Encoding 2794, Predict 1043, We are done with the cleaning and feature engineering 7192, let s create a new dataset only with normalized features to be combined with one hot encoded features latter 20625, We have a balanced dataset which is good 13116, CatBoost 38583, Every text be converted into machnie read able form 10144, Encode categorical features 20159, Having a look at pixel values frequency 36338, Evaluate accuracy 33329, Convert labels to categories 1890, To evaluate our model performance we can use the make scorer and accuracy score function from sklearn metrics 29366, SUPPORT VECTOR MACHINE 9970, Targets and features 36091, Discuss events 11633, K Nearest Neigbors 39171, When you are finished the CNN could look something like this 4331, Marital status 18396, Generate test predictions 42349, Retrain the model and generate the output file 30898, fill those values 319, Random Forest 25956, Training Data 1978, Data Cleaning 41366, Sale Price Pave Grvl 12078, Model Submission 22873, Model Training 38426, Improving network architecture 41438, look at the examples of if the tokenizer did a good job in cleaning up our descriptions 39422, get rid of the Name column for now 15397, Both are first class passengers that paid 80 26928, And then we can make two different corpora to train the model stemmed corpus and lemmatized corpus 10206, Lets check how much error the model is giving 15865, Random Forest 20389, Gradient Boosting Model 26403, We implement it here only for the training dataset to use it for further data analysis below 19416, that the model is defined one trick is to check that it works by passing one input sample 1238, Defining models 32924, let s generate the features coming from the installments data 16573, Evaluation of model with the best classifier 3338, As evident age is mostly correlated with Pclass this help us This time i use median age by Pclass feature and fill missing value by this correlation 1840, Distribution of SalePrice 3589, Feature Engineering 23404, Probability features 20056, Additional features 28702, Since Lasso performed the best after optimisation I chose this to be the meta model All other models be used as base estimators 1843, Distribution of SalePrice in Categorical Variables 26820, check the distribution of the standard deviation of values per columns in the train and test datasets 17529, Calculate average Age for each title and fill NaN values with it 11564, We have a few outliers that are pulling the distribution towards higher prices 14460, back to Evaluate the Model model eval 28071, Model building 27081, However this does not provide a great point of comparison with other clustering algorithms In order to properly contrast LSA with LDA we instead use a dimensionality reduction technique called t SNE which also serve to better illuminate the success of the clustering process 36591, CNN Model 18738, Lowercasing 26644, This means 2 2778, Ensembling Weighted average 18127, Gradient Boosting Regressor 27259, Create a DataFrame with our First Level features 4966, We can extract title and family names from the Name feature 37039, Is there a correlation between description length and price 13109, Decision Trees with Bagging 11920, Testing Machine Learning Models 2106, We continue with the top5 Lasso RandomForest XGBoost LGBoost Ridge 17901, Lets try Random Forest 22038, We have a timestamp variable in the dataset and time could be one of an important factor determining the price 14422, Use function age fillna to fill out NULLs for Age for training and test datasets 2580, Model and Accuracy 37719, Individual Fetaure Analysis 37905, Evaluation 2939, Transforming some Numerical Features into Categorical Featured 5502, Imputing Fare 42080, lets create a function which read the image using opencv library 42770, Cabin 40982, Alloccating Daily Revenue values into custom sized buckets by specifying the bin boundaries 40164, To complete our preliminary data analysis we can add variables describing the period of time during which competition and promotion were opened 39817, But won t it affect the image Same question was mine when i first thought about it but NO it won t Don t believe me See for yourself 33455, Non Graphical Analysis 6886, Gender 6437, Saving the data The final task 20540, Kernel Ridge Regression 23113, Findings Looks like most of the passengers over were single without family followed by passengers had a small family Almost passengers had medium families and just over passengers had large families abroad 28311, identifying the Catergical and numberical variables 16787, Feature Engineering 30601, Collinear Variables 12761, i check the number of Null values 15444, Create a new feature called FamSize which combines Parch and SibSp 22467, Bar chart 6202, Gradient Boosting Classifier 6785, Converting Numerical Age to Categorical Variable 22534, Embarked vs Survived 20860, RF 42084, Lets get a validation sample that is 20 of all training data should be marked as validation which be used to validate how our DL model looks 11494, 1 5552, Add predictions to submission 5925, Handling skewed data by applying log x 1 1 as if zero present thrn error 38512, Text word count analysis 18886, Feature engineering 32028, Fit the regressor 31047, Length of words 36255, Variable Name Description 42835, After training the model we plot the ROC curve on training data and evaluate the model by computing the training AUC and cross validation AUC 39988, Eliminating missing values 35444, CNN 7991, Impute 7999, Train Polynomial Regression 19901, Bottom 10 Sales by Shop and item Combination 10066, Variables SibSp and Parch give the information about passengers family hence we can add them up to reach the family size of the passenger including the passenger himself herself 18827, To make the two approaches comparable by using the same number of models we just average Enet KRR and Gboost then we add lasso as meta model 39978, Looking for Missing Values 11243, Skewness 16721, age 26178, first take a look at the distrubution of house prices 22501, placeholder which is use to initialize once when graph is run Basically placeholder is use for giving input to NeuralNet 11439, Tune Parameter 565, submission for GradientBoostingClassifier 39753, List out all the best scores 39154, Plot the 9 images with the highest loss 19305, Evaluation prediction and analysis 21327, Fireplace 12458, Scikit learn Linear Regression 3210, we have half and full bathrooms 38123, Checkout the size of data 15421, let s check if age group had an effect of survival 6114, Garage cars and Garrage area next please 5419, Modeling font 23364, Preview of first 10 images 37168, Addressing problems with NaN in the data 2360, Sklearn Classification Voting Model Ensemble 39455, checking missing data in credit card balance 22126, Bagging 35902, Ready to submit 15217, This got on LB 22129, AdaBoost 23197, Findings The prediction looks quite similar for the 8 classifiers except when DT is compared to the others classifiers create an ensemble with the base models RF GBC DT KNN and LR This ensemble can be called heterogeneous ensemble since we have three tree based one kernel based and one linear models We would use EnsembleVotingClassifier method from mlxtend module for both hard and soft voting ensembles The advantage is it requires lesser codes to plot decision regions and I find it a bit faster than sklearn s voting classifier 13110, Random Forests 8752, Create Output 40056, Personally I find it a bit easier to use weighted cross entropy loss but perhaps with tuning the hyperparameters properly the focal loss could be a good choice as well 4508, Data Cleaning 41873, Printing scores 12622, Model 26619, We are defining as well a function to process the data 7665, Clean and Edit Dataframes 16894, it turns out most of the people having Cabin recorded are from Pclass 1 23128, Age Survived 658, Based on the first look we define the input columns we ll be working with 8953, Fixing Fence 33880, Choosing the columns that we use as features 18369, Checking Autocorrelation 41576, Setting the Random Seeds 1225, Creating features from the data 27282, Base Model 19697, Epochs and Batch Size 16925, Rank all classifiers 39865, Sort out numeric columns 33253, Numerical features 15087, Extra Trees Classifier 30868, Building CNN architecture using keras sequential API 39310, Import and process training set 11821, SalePrice vs YearBuilt 42933, Removing data we no longer need 9785, From intuition 20 is a good threshold for now 14723, There is indeeed a stark contrast you were much less likely to survive if your title was Miss 917, Feature Scaling 19900, Top 10 Sales by Item 40144, also explore the possible correlations between features and simple high level properties of images without going into NN 32508, Training Model 2 29134, we can use resident Kaggler s creator of the Missingno package which is a most useful and convenient tool in visualising missing values in the dataset so check it out 37008, Most important Aisles over all Departments by number of Products 3241, Concatenating both the data set 18027, GridSearch Parameters 15775, Create new features 31557, doing same for GarageYrBlt LotFrontage MasVnrArea as train data 7630, blend 1 gscv Ridge and gscv Lasso 12462, Remapping Categorical variables 25163, Checking and Removing Null Value 42222, With the model set up there s just one more step to add before all of the individual elements can be compiled and that s to add the Optimizer 20847, that we ve engineered all our features we need to convert to input compatible with a neural network 15226, Handling Missing Values Imputation 17556, Its hightly skewed so we apply normal distribution to it 32043, AUC is a measure of the classifier skill considering all different thresholds 11464, Logistic Regression 18968, Display the contour lines of a 2D numerical array z e interpolated lines of isovalues of z 27555, Display interactive filter based on selection of dependent chart area 5669, Original Name 26110, Model Build Train Predict Submit 3586, Label Encoding 20977, Prediction 21167, split training data into training data and validation data 24685, All EfficientNet models can be defined using the following parametrization 30470, Ensemble multiple methods using VotingClassifier or VotingRegressor 39218, Run function 22699, Data Loader 7306, Observation 22403, is indrel which indicates 24701, BatchNorm momentum scheduler 25240, Log Transform 14256, Feature Engineering 31473, Train Validation split 1049, I didn t drop all the outliers because dropping all of them led to a worst RMSE score 16696, We do not require names after this either so let s drop that data 670, eXtreme Gradient Boosting XGBoost 31255, Hyperparameter Tuning 20909, Data Augmentation 36761, Splitting into Training and Validation Sets 25684, SIRD Model 34928, WordCloud for tweets 31120, Even there is no obvious charateristics on time derived columns month and date still have relatively high importance that s interesting 23440, Submission 4722, Import necessary libraries and files 11402, Looks like we can expect a model accuracy of about 80 41359, Kitchen quality is an important feature because there is a clear correlation with sale price 26662, Check the data 14323, Parch 6410, check relation of these fields with the target variable 36795, Awesome stuff But if we want to take it a step further we can We ve previously learned what lemmas are if you want to obtain the lemmas for a given synonym set you can use the following method 15208, Fare processing 35606, Show all problem images 5538, Statistical Overview on final Features 19053, We now specify the y or output using ColReader and specify the target column in the csv file which in this case 0 denotes benign and 1 denotes malignant 22672, Word Cloud 38671, Gradient Boost 14820, 1st class older than 2nd and 2nd is older than 3rd class 26256, Splitting data into train and test set 41420, Three classifiers are considered in this notebook Random forest classifier Support vector machine classifier and KNeighbors classifier 19846, Equal Frequency Discretisation 19534, Listing and Replication 42825, Sample 3690, Hybrid Models combine different models 10464, For a better distribution plot we can use the Seaborn package s distplot method which offers a smoothed histogram with a kernel density estimation plot as a single plot 17818, Model with Sex Age Pclass Fare features 26796, Defining the architecture 20137, visualise some digits 29752, Test set images 36340, Define Neural Network Architecture 42083, we create a training batch now 421, Scatter plots between SalePrice and correlated variables 1838, Categorical Features with Meaningful Ordering 36422, Filling Missing Values 19700, Evaluate the Model 14194, SVC 14649, Number of rows and columns 4201, We can use one hot encoding to create as many columns with 0 and 1 as variable values 29710, Before going any further we start by splitting the training data into a training set and a validation set 18801, Since area related features are very important to determine house prices we add one more feature which is the total area of basement first and second floor areas of each house 35117, create folds 28161, Training Evaluation 3953, Create TotalBath Feature 35427, Plotting the model metrics 15281, Survival by Gender and Age of pasangers 11358, Missing Data 18736, 2096320414714 5843, Pandas dataframe corr is used to find the pairwise correlation of all columns in the dataframe Any na values are automatically excluded For any non numeric data type columns in the dataframe it is ignored 23331, The errors are reasonable but the achieved accuracy is still a way off from the state of the art neural networks 9890, Name Title 1510, To do so lets first list all the types of our data from our dataset and take only the numerical ones 13698, Start by isolating the rooms which have data 14685, Training on different models 17442, in the future may ai check some better way to bredigt the age 14296, Writing the Output in csv file for Submission 34786, Count is hightly correlated with Casual and Registered 38998, Normalize the flattened array by 255 since RGB values are between 0 and 255 32273, Go to TOC 34046, Encode DayOfWeek to Integer 10569, Evaluate the training 22400, That number again 34413, Before we begin with anything else let s check the class distribution 12503, Tune subsample and colsample 16335, Support Vector Machine SVM 9184, Month and Year the house was sold 10422, Preparing to modeling 26809, Net 8526, Basement Features 38650, Total Family onboard 38308, Accuracy with Logistic is quite good 16648, Getting Data 39196, Tratando classes desbalanceadas 19913, XG Boost 27313, Training and Prediction 15183, ROC Curve and AUC 26422, Only bout 12 of the Misses younger than 15 years have no parents children on board 30680, lightGBM 39759, The second line of graphs is just a zoom in the interesting parts of the grpahs on the first line 14059, Pclass vs survived 42543, Feature EDA 34032, Cross validation is best option for this data set 1407, The Cabin Unknown be set to C for the first class D for the second class and G for the third class 14005, Logistic Regression 11129, More filtering of data to try 6851, now we analysis with base Parch Parents travel with passenger SibSp siblings travel with the passenger 16491, ENCODING Categorical Data 30684, Make a special function for RNN 18536, There are three two types of features 9673, Create a class to track all parameters and tuning process 9168, GarageScore 27127, The missing data may be as no data is available for the garage 43304, that there are no nulls in the categorical features I can proceed to label encode them so they become numerical features that allow me to put them through the regression algorithms 5697, Missing Data 1856, Ridge 30365, Looks no problem predict all provinces of United States which confirm cases greater than 500 13397, Modelling 40250, Same problem Same solution 1850, Normalise Numeric Features 20214, Normalization is an important step while training deep learning model 29325, There are many different columns now so it be difficult to view all of the data 12093, Y target value to Log as stated at Kaggle Evaluation page 16225, as S as occured most of the time so we fill it with S 34864, Combining All Columns 28111, Predict the Sale Price using the best fit XGBBRegressor model 22237, Title 28567, As the amount of exposure increases so does hte typical SalePrice 23998, Turns out we have dropped just one column Compare the shape 26492, Use the next code cell to preprocess your test data Make sure that you use a method that agrees with how you preprocessed the training and validation data and set the preprocessed test features to final X test 32727, Features Interaction 42076, Submit 13312, Decision Tree 28718, I start exploring our target item cnt day that refers to items sold and the item price 23801, Omit low information variable 38062, Feature NLP Engineering 39045, consider now the average interest levels for these 100 buildings 22089, Convert Numpy Arrays to Tensors 27964, Version 8624, Modelling 3707, Ridge 3664, Locating missing data 19252, Basic EDA 11202, Calculate Metrics for model list 630, As we would expect intuitively it appears that we are more likely to know someones age if the survived the disaster 14768, Final RF Submission 36197, Ok so maximum 6 months of data for customers with missing fecha alta It means we can consider missing fecha alta as new customers 29610, We can then get the learned values of these filters the same way we did for our version of AlexNet and then plot them 8702, no null value remain now 2202, applying these functions to both columns 3473, As mentioned earlier L1 regularization shrinks some of the coefficients to exactly zero and in this way does variable selection 30381, Feature selection 6186, The number of passengers survived on the basis of the siblings the passengers had on Titanic 25904, look at the important words used for classifying when target 1 3317, Observe the prediction effect of our model on the training set 19804, End of Distribution Imputation 31550, FireplaceQu PoolQC Fence MiscFeature 2336, Helper Functions 40112, Since we do have a lot of combinations we need manually assign a color palette 7986, Merge Lots 29130, Model2 Error 34252, Pytorch Tensors 15165, It s Time For The Machine To Learn 30965, As an example of a simple domain the num leaves is a uniform distribution 9754, FareRange 4848, Elastic Net Regression 23292, Highly corrolated features 31346, Our param grid is set up as a dictionary so that GridSearch can take in and read the parameters 9043, Correlation between Target Variable and Features 39092, Average syllables per word in a question 7679, We are done with the cleaning and feature engineering 5187, Logistic Regression is a useful model to run early in the workflow Logistic regression measures the relationship between the categorical dependent variable feature and one or more independent variables features by estimating probabilities using a logistic function which is the cumulative logistic distribution Reference Wikipedia 17913, DATA VISUALIZATION 37825, Word cloud for Normal tweets 7851, Explained Variance as a Performance Metric 3668, Check all numerical categorial 15535, Separate labels y from data 14823, Family Size 31801, updated Robustness Test with Albumentation 32699, Random Forest 20027, How can we use this 38409, Here we have a multiclass classification problem 8458, Evaluate Apply Polynomials by Region Plots on the more Correlated Features 36876, Keras CNN model 1 4807, Modelling 24314, let display the model errors 22519, Add also int label encoding for a train test pair implemented in the same manner 2510, Dropping UnNeeded Features 9587, Importing the data with Pandas 21172, plotting training and validation accuracy 34670, Cumulative revenue 5996, Kita menemukan Outliers di 25666, Cross validate the data so we can use a test set to check accuracy before submitting 25514, GETTING ENCODING FOR A PARTICULAR WORD IN A SPECIFIC DOCUMENT 31800, First let us test the first 10 test examples 32192, T SNE Lets code 21038, Topic Probability 8685, ALSO LINEAR REGRESSION IS BASED ON THE ASSUMPTION OF THE HOMOSCADESITY AND HENCE TAKING LOG WILL BE A GOOD IDEA TO ENSURE HOMOSCADESITY that the varince of errors is constant A bit scary but simple 27886, Roughly speaking this table is a summary of the previous three graphs 1944, Relationship with TotalBsmtSF 7070, I ll use the best models and put weight on their predictions 26734, Plotting sales over the week 27372, adding mean price and removing item price 31510, We sort the top 40 features 28612, Functional 428, MiscFeature Data documentation says NA means no misc feature 30938, Visualizing Outliers In Data 1210, Actual predictions for Kaggle 33634, A general overview of the data we need to work with 28941, The main reason why passengers who embarked from Southampton had a low survival rate was that most of them were 3rd class ticked holders 35625, Knowledge distillation 3453, assume the 693 3rd class passengers missing a Deck were in steerage 32070, Here n components decide the number of components in the transformed data 22078, Findings 1335, We can convert the categorical titles to ordinal 32481, Derive Recommendations 26970, Image Augumentation 8840, Prepare columns 32138, How to generate one hot encodings for an array in numpy 23823, y wrt ID of dataframe 34723, Submission 27202, Embarked 42002, Sort Columns by alphabetical order 31190, AUC ROC Curve 27371, working with the price 16291, Importing Libraries 28421, Simple graph 40682, look at another such feature distance from 10th most frequent template 29332, How do the predictions compare for the two random forests 27420, Less learning rate 29115, Our model was able to get to very high accuracy in just a few epoch s 26300, Test on validation datasets 40802, Prediction 9966, Distribution of lot area 9348, Predict age 9728, Looking at the neighbourhood spreads of lot frontage there are some areas in the city of Ames that have a fairly distinct spread of LotFrontage values 4286, We don t have 2010 data for all year round 12670, transformation 29363, Spliting the dataset 35172, Compile 10 times and get statistics 13167, let s load the datasets from competition inputs 467, t SNE 21274, Build roBERTa Model 42357, Removing https type symbol 32519, Train the Model 21524, We can now visualize this data 7292, Decision Tree 33512, Italy 8421, Test hypothesis of better feature Construction Area 37773, define a function that allocates large objects 39681, Display clustering boat 15263, Observation People from class 1 have higher chances of survival and People from class 3 have less chances of survival 36629, How about bathrooms and bedrooms 13797, Acquire data 8758, Corralation Of Data 8324, the accuracy turns out to be with n neighbors 42273, day 36468, Images from ETH Zurich 21012, Initial Model Building with dabl 28313, Let Exmaine bureau dataset 31546, BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2 1421, I create dummy variables for all variables with categories using the function get dummies from pandas 39789, Lets look at distribution of logerrors with top 10 frequent propertyzoningdesc 12098, Replace NA in Numerical columns based on information from description 36023, Presets 12138, Basic evaluation 2 41950, We are importing libraries nltk numpy pandas and sklearn 27609, Creating the OHE vector for the labels 1873, Dealing with NaN Values Imputation 37625, Distribution of labels 18576, The wider fare distribution among passengers who embarked in Cherbourg 23665, Beta t EGARCH models 35685, XGBoost HyperParameter Tuning 11889, Random Forest 29127, Performance Comparison Erros For Each Model 17644, Hyperparameter Tuning 22715, Plotting the image matrix 21794, Linear Discriminant Analysis 14390, We have to map each of the title groups to a numerical value 14454, go to Correlation section corr corr 3795, Concat all data 1575, The following two functions extract the first letter of the Cabin column and its number respectively 11548, How many missing values 4247, Tune 15636, Fare Jointplot 36679, max df float in range or int default 26292, Visualizing and Analysis 610, this is somewhat expected since it explains the difference between S and the other ports 31604, Exploratory data analysis 35498, Define Optimizer 40434, Onehot 40659, We now have everything we need for making some predictions 14875, From this data it looks like being a male or being in 3rd class were both not favourable for survival 27602, Fit Model Using All Data 35422, Defining Standard Neural Network 18564, group Andersons with 7 size family by ticket number 18507, I ve read a random image let s view the information inside 10301, I m going to come clean here 28658, LotArea 28655, Condition1 Condition2 27172, Similarly for test dataset 41945, Here s how the generated images look after the 10th 50th 100th and 300th epochs of training 6521, Replace the missing values in train set with median since there are outliers 31007, Data Exploration 3729, Transform the gender feature 7743, Submit 9234, Execution on Testset with Neural Network 20732, few columns need to drop 1259, Recreate training and test sets 41684, Make the submission 14263, Logistic Regression 12967, Embarked Sex Fare and Survived 26673, Numerical features 1583, take a brief look at our variable importance according to our random forest model 41103, Prediction for the next 15 days 24269, Predictive modelling cross validation hyperparameters and ensembling 38404, Just in case check the datasets info 29399, CORRECTIONS RULES FOR FULL SQ AND LIFE SQ APPLY TO TRAIN AND TEST 16978, Logistic regression 35754, choose method to use ensemble or stacked 2313, Model Prep Splitting DataFrames by Columns with 2 methods 41449, Latent Dirichlet Allocation 38928, MSSubClass Identifies the type of dwelling involved in the sale 40995, Clean both train and test dataframes 21858, Showing Samples 20502, Submit to Kaggle 409, KNeighbors 10387, Visualizing the relationship between Sale prices and Overall Quality 27742, Check for Class Imbalance 7735, Predictions 24468, Before EDA let s group the features into category and non category based on the number of uniqueness 35169, Plot the model s performance 31735, The Images 15345, Checking out the accuracy of our predictions 38652, Fare 34741, T SNE applied on Doc2Vec embedding 1141, GridSearchCV evaluating using multiple scorers RepeatedStratifiedKFold and pipeline for preprocessing simultaneously 7542, No missing value finally lets do type conversion 39140, Predictions 24416, Sale Type 13187, start with our variable Age to solve outlayers problems i dicide to divide passengers ages into 8 groups 19714, Compile the model 23884, check the number of Nulls in this new merged dataset 4467, Dealing with Age Missing values 40095, The model can also lemmatize assign parts of speech find dependencies and otherwise annotate text 30951, Train the model with cross validation 35768, Additional testing 10088, Skewness removal 8605, now we have sorted all the null values we can then proceed to create new features 38260, Number of words in a tweet 8072, NA s 37167, Encoding categorical features 35127, This data contains many outliers but these might have been caused to the surge of customers during a festival or Holiday or due to an effective promo 43015, Besides Logistic Regression therefore I used class CurveMetrics 12294, The embarked place is obviously connected with the fare and the tickets From the value counts the probability of S is more possible than other two options 35095, Statistical Analysis Machine learning 5281, Drop Column Importance 13489, Outlier removal 15201, Correlations 9932, Finlly I decided to use Ridge regression because performs better on this scenario 32477, We now have everything we need to perform memory based and demographic based CF 43073, Distribution of skew and kurtosis 20951, In Keras we can add the required types of layers through the add method 9881, Correlation Between Age Survived 33886, agregating Bureau balance features into Bureau dataset 7144, Random Forest 1256, ML models have trouble recognizing more complex patterns so let s help our models out by creating a few features based on our intuition about the dataset e 36927, Pclass 21332, Ph n c n l i 19149, Making a submission 705, remove the original categorical variables 43008, Check Null 11481, LotFrontage 23380, Data pipeline 16639, Preprocessing 15558, I fill this in with the median fare of the passengers for that 26498, Lastly we set aside data for validation 34028, New features Temp by weather 14336, We can put the cabin column in dataset but there is lot of missing value in it which may reduce the model performance 42212, up the first convolutional layer is added 24543, check number of products by channels 7802, Compare Models 42859, Callbacks list 38788, Data frames with new predictions 41783, CNN model Without Batch normalization 24238, A very first look into the data 24387, delete the columns that have more than 15 missing data 35132, The columns StoreType Assortment Season have char type or String type values all of this need to converted to a numerical value 22807, Educational Level Below Primary 35812, Item features 18543, Loading libraries 7803, Create and Store Models in Variable 26290, nn model The general methodology to build a Neural Network is to 22611, Random Froest 22513, It takes a while now is time to look at what goodness it gives to us 3946, Imputing Garage Related Features which are numerical with 0 and None for categorical 31201, Decision Tree Classifier 35139, Using Linear Regression to predict Sales 28183, Let s create a custom tokenizer function using spaCy 13343, SibSp Parch combining features div 10256, Go to Contents Menu 32830, remove the text noises 27241, There are peaks in average mortality rate trend due to China Iran UK and Netherlands which drops down in about 15 days The rise in Iran reached its maximum on Feb 18 however this is the same time when the outbreak started in Iran Here one should be cautioned as these numbers truely depends on the number of confirmed cases which itself depends on how many tests were performed during that time The average mortality rate in Italy and Spain is still rising to 12 Lets look at the mortality rate by the end of the training data date 32022, There are 2 null values in Embarked columns of train X 12320, OverallQual 4 1901, XGBoost Model 13139, Survival by Fare and Pclass 26184, Lets log the data so that it can be linear 4445, deal with the rest of the missing value 12516, First try to understand the data 4632, Filling in missing values 11276, Another way to engineer features is by extracting data from text columns Earlier we decided that the Name and Cabin columns weren t useful by themselves but what if there is some data there we could extract take a look at a random sample of rows from those two columns 6568, Other feature Age have missing value be calculated with mean is null and standered deviation of Age 4144, let s enjoy the number of algorithms offered by sklearn 10381, Fixing the missing data 20764, let use LGBM AS WELL 5287, As a final step in our experiments we are going to train a set of LR models with the top features selected by drop column method 34916, Count numeral in tweets 34155, Not as enlightening as I expected The data is too noisy in this plot as it accounts for the variation of each day s sales 40084, Exploration numbers of categories and values for categorical features 9440, The missingno correlation heatmap measures nullity correlation how strongly the presence or absence of one variable affects the presence of another 1848, Correlation Between Numeric Features 13446, Age Young have more chance to survive 40261, Total Basement Surface Area 7977, Modeling 9701, Train test Split 11049, We now implement the data preprocessing and have a look at out processed data 9823, From the number of the cabin we can extract first letter which tell us about placement of the cabin on the ship 39333, Text features for item categories df 41110, DataType Converting 42062, Using seaborn to display displot 28705, The new test dataset is 1459 rows of predictions from the 8 algorithms we decided to use 6636, Making some changes in the titles 40860, Feature Scaling 27243, To do Display Confirmed cases by Population 6454, Pipeline 2988, Lasso regression is a type of linear regression that uses shrinkage 41662, Categorical features 19951, Family size 22390, Plotting the training history to tune parameters 14105, center Boxplot with hue center 8935, Generating Validation Set 12564, First we remove the unwanted features from our data 34008, atemp 8705, Splitting into Training and Validation Sets 15089, CatBoost 12362, Kitchen Variables 29369, DECISION TREE 31917, Train Test Split 5105, Observations 39138, And since softmax is a probability distribution it sums up to 1 3242, Estimating the location for our target variable 23025, The price of some Household category is super expensive like over 100 13967, Embarked Survival probability 27526, Display heatmap of quantitative variables with a numerical variable as dense 38227, Address 12359, Masonry veneer type 583, Simple clustering 16116, Support Vector Machine SVM 29002, Only women from who embarked in either Cherbourg or Southampton survive 28800, after the transformations our p value for the DF test is well within 5 Hence we can assume Stationarity of the series 8846, For the most skewed column let s look at the distribution 22822, And there exists a value of 1 19004, Set up HyperParameter search 22231, Elimizdeki verilerin farkl rotasyonlar n alarak verilerimizi o alt r Data Augmentation 21442, Check up Missing Values 40673, Histogram plots of Number of characters 18078, plot some examples of large bounding boxes 8955, Fixing SaleType 41010, All these people have the same Pclass Fare Embarked and Ticket number but two of them are considered part of any group this is what we are going to fix 23941, Word count 26885, Score for A3 16423 30716, Reshape image into 3 dimensions height 28px width 28px channels 1 18276, XGBoost 33684, Add Hours Minutes font 9676, Tune min child sample weight and min data in leaf 1907, Heatmap 20942, Sample 10807, I think it looks obvious that low fares had less chance to survive 40169, The next step in ourtime series analysis is to review Autocorrelation Function andal Autocorrelation Function plots 36991, let s identify which products are ordered the most 1870, Importing Libraries and Packages 17545, 2 Entries of Emabarked Column have Null Value 18552, Actually Mr Algernon Henry Barkworth was born on 4 June 1864 16049, Survived Count 27459, Remove Stopwords 26795, Adding dimensions for keras 35570, Merging the data with real dates 206, Model and Accuracy 24110, Data Preprocessing 27858, Uncomment below if you want the predictions on the test dataset 848, KNN Regressor 16848, The Sex variable 33520, For severity level 4 I feel that two examples here are difficult to spot on pic and pic 28038, PREPARING DATA FOR USING IN KERAS 17816, The accuracy for the validation is much better than the accuracy for the training set 10586, Visualizing AUC metrics 29404, CHECK STATE 14091, First Submission File 10676, Prediction 33682, Difference Minutes font 42765, Embarked 37080, Different Ensemble Methods 14212, Data Dictionary 40695, NOW THE DATA EXPLORATION ANALYSIS AND VISUALIZATION AND PREPROCESSING HAS BEEN DONE AND NOW WE CAN MOVE TO MODELLING PART 42130, Fine tune the complete model 8660, Instructions 28806, We ve correctly identified the order of the simulated process as ARMA 2 2 15626, Free Passage 20640, Lets plot word cloud for real and fake tweets 5160, Support Vector Machine 110, is alone 20209, Model Building 10932, Degree Centrality 3914, Utilities For this categorical feature all records are AllPub except for one NoSeWa and 2 NA 5245, Label Encoding The Categorical Variables 33437, even with topic modeling there are only stopwords they be removed for better visualization of relevant words from the data 19643, Gender ratios by phone brand 13036, Name 460, Submission 19129, Blend 17996, In other cases the family members were not traveling on the same ticket 32705, tokenization 19584, Implement Lags 5286, we are ready to train one more set of LR models that use top features selected by permutation method 19829, James Stein 4061, We can confirm that if we have a master then the absolute value of the correlation be significant and we have already found a simple way to threat it 38027, Modeling 21526, Multilayer Network 28026, Or in more related way to competition challenges 16677, Read data into Pandas Dataframe 38823, Load the training and testing data 26461, Extracting Zip file sec 12496, Submission 16550, encode the age column into 4 parts 16637, Visual Exploration 12155, The same we can setup with two pipeline branches for numerical and categorical data 17775, Go to top font 31859, Select best features 4565, Splitting X into X train and X test 5392, To be more precise we need to understand the meanings of each columns font 1658, A continuous feature here for a change right skewed with a peak close to zero 8682, FEATURES WITH MISSING VALUES 41052, Number Of Product Department 41851, First Patient empty lung 5111, Predictions 21676, Blender call 10865, Converting the categorical columns into numerical 33659, Define X and y 4643, Since we are working on a supervised ML problem we should also look at the relationshipt between the dependent variable and independent variable In order to do that let s add our dependent variable to this dataset 23473, Retraining the decision tree over the whole dataset for submission 25390, Train the model without data augmentation 3605, Missing Data 14920, Statistical Analysis and Feature Selection 23942, Category Name 5425, the missing values for the MasVnr 11302, Blending 7375, Preparing Wikipedia dataset for merge 5303, Handling categorical data 42077, Uninteresting half bathrooms 14776, Name 11321, SibSP 29689, Adam with default lr 1 and different weight decays 13071, Bernoulli Naive Bayes 9146, For basement exposure I just set it to the average value based on both the training and test set where HasBasement is true 236, Preprocessing 43394, that s the same pattern as for the fooling targets 20358, A low difference means the classifier is unsure There is a Jupyter widget PredictionsCorrector which then serves up batches of the most unsure predictions for the human analyst you to confirm It looks like this 6715, Find Continous Numerical Features 23675, Reshape normalize 36567, Prediction 20168, Case 4 Binary Dimensionality Reduction PCA 4487, Decision Tree 18530, The most important errors are also the most intrigous 28116, Evaluation Functions 28250, who accompanied client when appliying for the loan application and their repayment count given below 21593, Convert continuos variable to categorical cut and qcut 29703, you have two Options the simple one is to just take the best Model of our Grid Search and make predictions on the test set 37901, Best Score and Parameters 16843, Feature Engineering 37739, Object types optimization using categoricals 39444, POS CASH balance data 27017, GroupKFold 34623, After the training it is a good practice to visualise how the cost curve evolved as the number of epochs increased 26685, impute missing values 12413, GarageCars GarageArea 35558, Parameters 2428, Decision Tree Regressor Model 11429, Rules Of Thums in Imputation 8420, Check the Dependent Variable SalePrice 35355, We have 28 28 784 pixels of the images 34658, Price distribution 16861, Moving On 11178, The shape values are the number of columns in the PCA x the number of original columns 33657, Stratified KFold 12111, Saving DataFrame for next Steps 22048, build xgboost model using these variables and check the score 36134, Imports 18402, Bigrams 10122, Feature Engineering 42857, Model parameters 21950, Split training and valdiation set 25691, Meidcal Facility Indian Heatmap 2719, Ridge regression 21651, Creating toy df 3 methods 6148, for some gradient boosting machines let s encode categorial string values to integer ones 24584, Create the Optimizer 5905, Q can k fold cross validation be applied to regression 21098, Visualize data First try to visualize some random samples extracted from the data I m using different methods which we can use to visualize data in tabular way 2177, Ups 5423, Alley 38263, also visualize the wordcloud 28198, of Speech Tagging 32649, Specific numerical features have a discrete nature 38224, there is no significant deviation of incidents frequency throughout the week 29769, visualize the images from the validation set that were incorrecly classified 21579, Trick 86bis Named aggregations on multiple columns avoids multiindex 35487, Data shape 899, The Classifiers with best performance are Decision Tree Random Forest and SVC 22845, First let s insert the date block num feature for the test set Using insert method of pandas to place this new column at a specific index 11855, We right our wrongs move forward and repeat this process again and again 25011, Revenue 28518, TotalBsmtSF 16307, Below I have just found out how many males and females are there in each Pclass then plotted a bar diagram with that information and found that there are more males among the 3rd Pclass passengers 5405, And now let s dig deeper with whether these people survived or not 17751, I m setting the random state variable to prevent random fluctuations appearing significant 18214, Agora montamos nossa rede neural 672, For each of these various classifiers we can have a closer look to improve their performance and understand their output 7720, Data Preprocessing and Cleaning 12531, Creating diffrent Models 38076, Test prediction and submission 42205, Predicting Price 23307, Final Model 22414, The next columns with missing data I ll look at are features which are just a boolean indicator as to whether or not that product was owned that month 19261, Remove unwanted columns 16006, Removing remaining features 3831, describe function is use for details of all statistics 38641, Data Assessing 4863, Deleting the 2 outliers in bottom right 20958, Import Training data as Numpy array 40919, Prepare data for macro model 28177, Named Entity Recognition NER 34090, Combined Features 18275, LINEAR SVM WITH HYPERPARAMETER TUNING 11707, Now after checkig the data lets go to Clean 22291, Building the graph 3991, Select columns with high correlation to drop 42013, Creating a new column 41174, callback Learning rate finder 4430, Target Variable 1989, XGBoosts Classifier 32895, that we have our similarity matrix let s calculate the pair match score 5078, Remember the outliers Dean De Cock warned us about Removing these now 34395, We have far too many points to plot so I ll try a different approach 23685, Sample digits 14981, As there is just 1 missing value of Fare in Test data set we can fill it using Mean or Median 21812, Prices 2103, Or use this plot to just investigate further the features we have analyzed before 3942, Find the total and percentage of missing values in dataset 27236, Train the model 5297, we can use RFECV learn org stable modules generated sklearn feature selection RFEChtmlsklearn feature selection RFECV to eliminate the redundant features 7549, Decision Tree 40197, Build CNN Model 22792, Data Fetching 9911, Get Model Score from Imputation 21535, Logistic Regression 32209, Add lag values of item cnt month for month item id 15161, Nowww Time For Some Plots Charts 8969, Cabin 20753, PoolQC column 24764, As well as scaling features again for the Kernel ridge regression I ve defined a few more parameters within this algorithm 38523, Preprocessing data for Bert 29746, Read the data 32962, Highly correlated features 6132, This house was built in 1940 5153, Feature Selection 24760, An addition to the Lasso model I use a Pipeline to scale features 28205, We now check if theres is some data missing 20782, Checking for Missing Values 14235, Find here an Ensemble of 20 Benchmark ML models including tree classifiers GBDTs SVCs Naive Ba and LDA Classifiers 15413, let s have a look if passenger class is a good predictor of survival 1987, Decision Tree Classifiction 8555, Describing the Data 1807, Observation 8715, Use no Age data at all 34098, Trends of Top 5 covid 19 affected countries and India 11382, Triggering the Engine 7221, Replace missing with most common value 827, Dropping all columns with weak correlation to SalePrice 8892, Actual vs Predicted 4276, GarageYrBlt 2915, I delete these features because I created the dummy variables 27302, How does individual product ownership evolve over time 37043, Test Time Augmentation 15192, Modeling 32903, Shape of the matrix 20489, Cash loan purpose 23875, This looks nice with some outliers at both the ends 35863, We have hit 35654, use the stacking classifiers predictions as submission 3000, Modeling 19976, MLP with Sigmoid activation and SGDOptimizer 28528, 2ndFlrSF 20465, Total income distribution 5155, Identify the selected variables 10719, Believe me this is the most interesting thing i found 35635, build an autoencoder in keras It have 3 hidden layers with 64 2 and 64 units respectively 29449, In natural language processing one of the best method for text classification is using pretrained text 42268, bedrooms is categorical column 22843, we need to merge our two dataframes 25395, Confusion matrix 13424, Stacking Ensemble 37020, Zoom on the second level 26342, Predictions and Submission 27457, Replace Negations with Antonyms 42074, Detecting best weight to blending 12207, A pipeline for categorical features 11748, we have filled in all of the obvious missing values in our dataset as outlined by the data description 1631, Filling NAs and converting features 16567, Observation 28289, prepare batch generators for train part 6680, Extreme Gradient Boosting 35133, Are the promos effective 14751, Embarked Missing Values 25432, Final Dataset 28310, Examine application test data set 18988, Load the word embedding model 42752, In order to create our embedding model we need to have a look at the spatiality of the cat features 13197, let s drop the ogirinal Parch and SibSp variables 6662, Logistic Regression 22759, MODEL 15498, Looks like the main ones are Master Miss Mr Mrs 10526, Although their type is Integer We would treat them as a categorical feature in the next section EDA on Categorical Features intLink 14287, Confusion Matrix 24528, Most of the customers used only one product which is the current account 42822, Training Function 35610, Downloading 17964, pandas 21202, More Data 14590, Gender 28027, X Y Z 16583, Lable Encoding Categorical Data 36295, Support Vector Machine SVM 5838, Lets now understand how the Housing Prices SalePrice is distributed 12238, Dictionaries 36200, we ll use the padded token IDs to create the training and validation dataloaders 38841, Plotting by segments have revealed different patterns 2819, rf feat importance that uses the feature importances attribute from the RandomForestRegressor to return a dataframe with the columns and their importance in descending order 26429, We already introduced these feature in section 4 32159, In fact we can just do nothing 37884, Top 10 Feature Importance Positive and Negative Role 7697, ElasticNet 26015, I personally consider dealing with missing values is very prominent as it can significantly affect the size of the data from the ML model perspective 36367, Final submission file 905, Linearity and Outliers Treatment 2126, While the performance on our test set is 12117, Submission 35106, we can split it into a training and validation set 15815, survival percentage of women is more than the men 11999, let s check the feature importance for ridge regression 11979, create one new feature that is age of house for simplicity 27546, Go to TOC 16475, Data Visualisation of Entire Data Frame Training and Test Set 41795, Create our CSV file and submit to competition 2349, Build Full Model using best parameters 1645, Explore features properties 11514, Support Vector Regressor 32544, Binay Columns 10113, Here it s clear that people with more number of siblings spouse onboard had a lesser chance of survival 13267, Statement Everybody from the class cabins that were sat in Southampton S were died 17931, Sex 1112, Feature importance selecting the optimal features in the model 2978, Missing data 36794, If we feed a word to the synsets method the return value be the class to which belongs For example if we call the method on motorcycle 13375, Embarked 15919, Survival of the Group 22392, iv Plot the test image which is being predicted 11080, Visualise co relation between classifiers 5483, check missing values in our independent and dependent variables 36084, Evaluation 30102, Data Augment 3808, SVR 10496, look at Test data 41024, Non WCG passengers 40838, Using the XGBoost Regressor for predictions 873, Fare continuous numerical to 12 bins 17470, Ensemble Model 13186, Clearly passengers from all classes can survive but almost passengers that died was from second and third classes 14056, Loading Data 14882, Sex 14651, Create a column Cabin Status 0 where Cabin is NotAvailable and 1 otherwise 34024, Temp Atemp 14824, Small families have more chance to survive than large families 21677, Dimensionality reduction 812, List of features with missing values 10670, Plot the model 33646, Dealing with highly skewed variables 26680, EXT SOURCE 3 38558, Exploratory Data Analysis 6597, look at the first 3 corresponding target variables 1787, Describe the Datasets 12202, A pipeline for numeric features 7078, the PassengerId of the female Dr is 797 232, Model and Accuracy 33256, Encode 31567, we can use dataloader as iterator by using iter function 28089, Create submission file 36786, Not sure what DT JJ or any other tag is Just try this in your python shell 43353, Classification Matrix 25511, embed the words into vectors of 8 dimensions 5325, Display density of values with heatmap over latitude and longitude 24001, Here we compare the various models that we just created 31316, Merging Data 26538, visualize the training process plot attached separately in comments 24175, Pre Proccessing 37699, A is 10 what else 33293, Deck Finder 35601, It is similar isn t it 40394, The code below is commented out as it was already defined earlier 43196, LGBM training 4598, SUMMARY 21413, Featuretools is an open source Python library for automatically creating features out of a set of related tables using a technique called deep feature synthesis Automated feature engineering like many topics in machine learning is a complex subject built upon a foundation of simpler ideas By going through these ideas one at a time we can build up our understanding of how featuretools which later allow for us to get the most out of it 15134, Again They behave u Lady elderly First u even if they were in desperate situation 40406, Work in progress 10960, Missing values 13519, Balancing Data 12096, Create columns to mark originally missed values 4153, Replacement by Arbitrary Value on Titanic dataset 2392, Examine each step of a Pipeline 16354, Look at feature importance 6058, Utilities irrelevant I can drop it 27945, Rankdata 29061, Krisch filter 17786, There are male only titles Capt Col Don Jonkheer Major Master Mr Rev and Sir 33252, Imputation 21629, Convert from UTC to another timezone 21405, Building the Convolution Neural Network 27388, Tuning number of leafs 11259, The final model be created and trained on the predictions of the models on the validation data 6787, more than 50 of 1st class are from S embark 19556, Lets predict on test dataset 28523, MasVnrArea 7944, have a look at the architecture and the number of parameters 22682, score 18760, Nodule Candidate Region of Interest Generation 33237, Prepare X and y from the dataset 33305, ROC S of the Models 17, RandomizedSearchCV Lasso 4419, Plotting the cross validation score 9436, Seaborn s jointplot displays a relationship between 2 variables as well as 1D profiles in the margins 2086, We implement a cleaning procedure that follows the documentation shortly but before even creating the evaluation environment we want to remove 2 outliers that the documentation recommends to remove 41394, NAME CONTRACT TYPE 4711, Combining predictions and submition 31114, delete columns with more than 20 missing 6324, Ensemble Voting 23929, This is working surprisingly well 23369, Some of the Incorrectly Predicted Classes 26677, Education Type 10109, Let convert our prediction int Submission csv 37919, Data Cleaning and transformation of test data is done using proper analysis with respect to other co factor variables 7584, The two new features are highly correlated 6346, One hot encoding 37437, Readability features font 8541, KAGGLE SUBMISSION WITH TEST DATA 11074, Voting 17769, From the total female passengers 74 survived 5993, Correlation Matrix 8562, Finding the Minimum Maximum Sum Average and Count 18950, Display time range for labels 38907, Modelling the Tabular Data 22530, Pclass vs Survived 34603, PyTorch s style data loader defintion 21454, All Feature 22179, BERT Base Uncased span 28498, try SHAP 4774, Support Vector Machine 32971, fill the missing values in Embarked with the most frequent value in train set 4747, Categorical Features 240, Library and Data 21062, Prediction on Test Set 20792, We also have useless features so we also decide to drop the features below 6469, Evaluate non ensemble Baseline methods 42018, Creating a new Dataframe with certain columns and certain rows 41926, Feature analysis 23258, Parents Children have higher chance of Survival 37111, Sample code for regression problem 25522, How d We Do 6935, Corrplot 5044, How are numerical features correlated to SalePrice 138, Using best estimator from grid search using KNN 36236, Soft voting can be easily performed using a VotingClassifier which retrain the model prototypes we specified before 40141, optional Undeploy model 2240, Transforming our Data 12557, SVM 11746, There are also some variables that are numeric variables instead of categorical variables that do not exist in certain houses 30881, Group data by month to visualize variance per month 3464, Final preparations for model building 5609, Kruskal Will Test 13349, Model Submission div 6586, we can evaluate our model to choose the best one for our problem 6932, Numerical data 15067, Age Group 18268, Basic Feature Extraction before cleaning 12240, Loops 42355, text cleaning 26649, Since this is a sample from page views 41534, Here the first principla component is responsible for all the variation 36793, Background 28285, Way to transform dataframe rows to torchtext examples 10105, check NULL values in Test dataset 18932, Relationship between variables with respective to time 10515, Now Apply Models 8367, Drop broaden variables 42728, find the float features which are highly correlated with target 8220, Integer type variables The missing values would be added with median values 31651, Submission File 8761, Survival By Class 24958, Embarked in Train Test set 32994, Plot PCA reduced picture 11420, Use Case 12 India Blood Bank Visualisation 24432, Fitting the model 7908, The target column is skewed Therefore we need to transform it into a more normal distribution since linear models perform better 22097, Average Loss VS Number of Epochs Graph 2379, Imputing missing values for categorical values 20072, Insights 15449, ML Predictions 4252, Nominal Features 6274, I locate each of the bins using 6581, Age Multiplied with Pclass 27287, Load populations of each country 21368, Scrap 40198, Accuracy and Loss Graphs 32080, Figure 4 Distribution of mode proportions across categorical variables 5564, Congratulations now we don t have any missing values in our data 31831, We ll use to resample the minority class 19061, We can look at the top losses 14912, of the Cabin data are null for both dataset 32577, Complete Bayesian Domain 3265, visualize relationship of features with SalePrice using Seaborn s Heatmap 20712, Street column 9000, Location Location Location 2261, First I drop PassengerId from the train set because it does not contribute to a persons survival probability 34419, First let s check tweets indicating real disaster 24186, Now let us deal with special characters 7387, To understand the issue I looked for the WikiId 1128 in the final DataFrame with all matched passengers merg all 19857, The upper boundary for Age is 73 74 years The lower boundary is meaningless as there can t be negative age This value could be generated due to the lack of normality of the data 26330, Predict on test data 22641, Model 5 Random Forest 36415, Countplot Discrete To find features with single values and remove them 6048, Type Quality and Condition 20158, Converting D array to D x array using reshape reference generated numpy reshape html to plot and view grayscale images 29441, Keyword 35916, Plot the loss and accuracy curves 6027, I use mode for cats and for median for numeric features but you can change it whatever you want 25954, Orders csv 11991, RMSLE 24026, After preprocessing is done I combine everything into one dataframe 37014, How many items categories do we have 32395, These functions below for reading labeled tfrecords 12338, Garage 25837, Looks more url counts in disaster Tweets 4726, At first i want to drop Id column 24391, Feature Engineering 24924, Here is an illustration of the process 30652, FATALITY 36662, From the scikit learn documentation learn org stable modules feature extraction htmltext feature extraction 37484, K nearest neighbors 20070, Insights 33490, Germany 27936, Inference 402, XGBoost 13266, Statement All boys Master from the classes survived 35507, PREDICT TEST DATA 13113, SVM with RBF kernel 34973, accurate submission 4507, Zooming a little 43286, O R uma m trica que j est incorporada no quando usamos o par metro oob score True Para acess la basta usarmos o comando abaixo 30626, Parch is a numerical variable 35389, Define Model Architecture 2553, Using the title Master to create a column VIP 38089, For building Neural network I am using python keras library tensorflow backend 4552, GarageType GarageFinish GarageQual and GarageCond Replacing missing data with None as there may be no garage in the house 12149, We can tune the scoring by providing several parameters 33327, Test data preparation 10530, But this new TotalPorchSF feature is not linear with SalePrice so we not use it 11448, First I thought we have to delete the Cabin variable but then I found something interesting 1240, Validating and training each model 29553, PyTorch 2817, resplite the training and test set 1682, SpiderMan ah that is some useful info OverallQual feature got 10 categories 37616, Random Forest Regreesor 10194, Analyzing most important features 28994, find out the relationship between categorical variable and dependent feature SalesPrice 7687, Last thing to do before Machine Learning is to log transform the target as well as we did with the skewed features 4363, If we use this new feature we must remove BsmtFinSF1 BsmtFinSF2 feature as we have already use it 31098, TO MAKE CSV FILE FOR SUBMISSION 5318, Garage Area and Garage Cars as well as TotRms AbvGrd and Gr Liv Area are highly correlated 927, Optimize KernelRidge 13028, Ticket 1990, ANN 9417, GridSearch 33805, Several of the new variables have a greater correlation with the target than the original features 16366, Predicting Ages based on grouping by Sex and Pclass 32023, The most frequent class is S so we can fill null values with S 19615, Redundant features 8450, Check for any correlations between features 17886, Lets visualize the relationship between the target variable and independent variables 18596, that we have a good final layer trained we can try fine tuning the other layers 5242, Creating New Features 5123, Create baseline model 13245, Missing value fill 21845, If we unfold the RNN 3433, The passenger was a 3rd class ticket holder 37733, Predicting the probability of every customer is unhappy 35866, I be using a sequential feed forward model e the data flows from left to right there are no feedback connections 28068, And drop the unwanted columns 37339, Voting function did not find the kind of direct vote on the forecast I do not know if there is I wrote one of my own 29819, Embedded Matrix Keras Embedding layer 38087, The cross validation is a techinique used for measure the accuracy and visualizing overfitting 15987, Logistic Regression 39289, Feature analysis 11400, And finally in this simple kernel we won t investigate a passenger s name nore their PassengerId 13441, Fare when Fare value increase 3611, Looking at the relationships between qualitative variables and Sale Price 17523, Pseudo Labeling 2223, Neighborhoods 40877, Looks like GrLivArea is the the most positively correlated factor for SalePrice while MSZoning C all is the most inversely correlated factors for SalePrice for all the three models 16691, start by dropping unwanted features 30356, Predict World Without China Data 15744, cross validation 20282, Pclass 27634, The top cross correlated entries are 19978, for relu layers If we sample weights from a normal distribution N we satisfy this condition with 3770, Age 33710, FEATURE Survived 22326, Removing Additional Spaces 13505, Model 42792, Meta Features 7551, Random Forest 36222, Comparing the MAE of both models we can say that the XGBRegressor model works better we use this model for our final predictions 2948, Train Model 1 40190, Using my notebook 26357, Data Preprocessing for Model 40010, Age distributions 35867, Initiailising parameters 37800, Predictions from our Model 13118, Evaluation Metrics 4964, We can discard PassengerId since we assume the passengers are sorted at random 32238, now let s make some predictions with the test set 19654, Make a submission 36838, Utility functions 28875, Normlize 23352, Learning Curve 88, Gender and Survived 16923, ExtraTreeClassifier 19329, Encoding train labels 2932, Ensemble Feature Importances 32984, Support vector regression 2723, We obtain a score of 0 9895, sum up SibSp and Parch to get the family size 1570, Embarked 30753, Fix max samples leaf 43316, we shall apply the algorithm and check the accuracy 10193, Looking for most relevant features 12058, Categorical Encoding 30825, MAP calculation 38534, Set the optimizer and annealer 27117, PoolQC MiscFeature Alley and Fence are the categorical features with more than 1000 missing values in the dataset 14484, 25 QUARTILE IS 7 THEN 50 IS 14 75 IS 31 MAX 512 36360, Probabilities can be identical for several values as pointed out by Commander 15925, Numerical values 33751, Appendix 14728, Feature Selection 12717, Round 1 complete and it is the Gradient Boosting algorithms that come out top 15093, ROC Curve and AUC Score 18200, a quick check if demand distribution changes week to week 9397, Objective function 29472, Analysing extracted features 26812, Baseline model 9306, The wrong way of handling dummies 3439, Aside from those special titles we have four categories Master Mr Miss and Mrs 7948, No sign of overfitting by the evolution of the validation vs 40955, Producing the Submission file 30710, Plotting a few images with and without augmentations 794, Our dataset is now much cleaner than before with only numerical values and potentially meaningful features 38081, Train and Test Matrices 14857, Modeling 41403, this means that only 278 loans have some other type 11661, Random forrest 28538, I divide dataset to 2 parts 26565, let s try to rotate the competition dataset systematically 18106, Since the test set is not that large we not be using a generator for making the final predictions on the test set 33787, Aligning Training and Testing Data 30569, We need to create new names for each of these columns 7136, Name Title 5499, Understanding the Survival Nature of Titanic 23734, Family Size Feature 24260, Visualising updated dataset 22664, Target Distribution 13712, More than 77 of the values in Cabin are missing Since it is impossible to replace so many missing values without introducing errors we remove the feature named Cabin 10850, Split data into two parts for training and testing 3811, Stacked Models 36137, Preparing the data for Modeling 29009, Plotting the graph 26295, Labels are 10 digits numbers from 0 to 9 30577, First we one hot encode a dataframe with only the categorical columns 43056, The next 100 values are displayed in the following cell Press Output font to display the plots 4567, Predicting test file data 6306, Multi Layer Perceptron 34909, Link Flag 13614, we apply target map to Honorfics feature 19669, We can calculate the number of top 100 features that were made by featuretools 12620, Assigning datatypes 9247, Percentage of Nulls calculation 28356, Analysis Based on CREDIT ACTIVE CREDIT CURRENCY CREDIT TYPE 31001, create additional variables which are simplification of some of the other variables 9582, Printing the version of the Python Modules used 33724, Mean Encoding feature 22437, Jittering with stripplot 13108, Decision Tree 9183, Sum of years since remodeling and built 28075, Linear model 18199, lets aggregate by week and short name now 13401, Feature Importance with Random Forest model 17712, Lets create train and test dataset and create holdout set for validation 16248, Library Settings 11173, Try dropping the lowest correlating columns 8898, RidgeCV 29983, Validation dataset prediction 33147, As expected some of them are quite similar to specific digits archetypes while others still contain generic and undefined shapes either because they lay closer to a border region between different categorical clusters or simply because in need of more training 1426, KNeighbors Classifier 18455, Setup the model font div 35413, Attribute Geographical information latitude longitude 40252, Total Basement Surface Area 12208, The pipeline for categorical features then be 4920, Box Cox Transformation on Skewed Features 24679, Training 2921, Logistic Regression 31192, Grid Search on Logistic Regression 35626, Experment 2 18187, ROC curve 5505, Creating a new feature based on Age 22350, Support Vector Machine 22471, Cross correlation plot 10371, Outliers 10466, We can also estimate the PDF smoothly by convolving each datapoint with a kernel function via the Seaborn kdeplot method 2093, we removed the skewness with that logarithm transformation in the previous section 9694, check missing values in numeric columns 31719, Comparing the models 1885, We can combine SibSp and Parch into one synthetic feature called family size which indicates the total number of family members on board for each member 9659, Fixing Skewness skew function returns unbiased skew over requested axis Normalized by N Skewness is a measure of the asymmetry of the probability distribution of a real valued random variable about its mean 25301, From these 2 binary morphology tests it is clear 23728, Almost 65 of the travellers are male and 35 are female 40482, Decision Tree 22357, BinaryEncoder 32472, Residual Analysis 8429, Masonry veneer 22008, Run the next code cell to get the MAE for this approach 41954, Remove numbers 10980, We notice there is two point are far from regression line and these two may effect the study and make mislead to the predicted data so the solution here is to remove them 29032, Save target separately for training data 15578, we have only seven titles 26239, Generating Pseudo Labels 10808, And now we are ready to start a prediction 6178, The distribution of age among the passengers and their count for particular number of age 8439, Back to the Past Garage Year Build from 2207 35815, Basic lag features 23041, Calendar Visualization 2973, Low Range SalePrice less than 143000 12668, I encountered an error when I attempted to predict classes for the test set because the Fare column contained empty values e NaN This was simply resolved by adding an extra line of code to the existing pre process function in the previous cell 33152, Evaluate on validation set 2053, XGBoost Model 36973, Summary Stats 13332, Name extracting information from this feature and converting it to numerical values div 30468, You can access a part of a pipeline using Python slicing 8390, importing necessary librarys 987, Set up our dataset preprocessing 37664, RAM Data augmentation 2930, The Learning Curve refers to a plot of the prediction accuracy error vs the training set size ie how better does the model get at predicting the target as you the increase number of instances used to train it 4028, Correlation between the variables 37067, the tactic is to impute missing values of Age with the median age of similar rows according to Title and Pclass 17755, I ll tweak certain parameters one by one and repeat this process looking for an increase in mean validation score 11061, Pclass versus fare 32213, Add lag values for item cnt month for month city 24774, Training Data 39226, Brute force approach 2554, Exploring Fare and creating categories 29601, Defining Necessary functions to Train the model 10412, Check for skew in the sales price vs transform with log 35936, Name 18479, that we are done with clearing missing values let s merge the two datasets 23894, YearBuilt 20387, Models Bulding 34836, Analysing and processing Categorical feature 36100, Load embeddings 35121, Model 18131, Ridge Regression 35416, Inspect your predictions and actual values from validation data 3835, scatter plot 6890, To look at the correlation between passenger class and survival statistics I would plot a countplot 35873, Checking validation loss and acc 25194, split our data into training and validation set and we are going to use the train test split function of sklearn library for this step 34103, Counts over the time 35513, Another features were created as a categoric variable 28965, analyse the continuous values with data visualisation to understand the data distribution 6855, Correlation Between The Features 40335, We ll use LightGBM model boosting due to its natural strengths 9303, Lasso experiment regularization 37562, 2141 feature columns 39974, Submission 5454, Since Every Tree is Different every prediction for the same point vary as well 38481, Display some examples font 24367, Seems variables are found to missing as groups 8714, Missing at Random 5540, Create basic SVC model 12179, Filling the Embarked feature missing values 20436, Model Build Train Predict Submit 29908, Adding Batch Normalization 16363, Lady the countess Mme Mrs Ms Mlle Miss Johnkheer Don Capt Major Col Rev Rare Sir Mr 10661, Interpretation 27227, Adam Optimizer 34104, Worst hit states in tree plot 35890, Imports 11853, One Hot Encoding 12000, making predictions on test data 24558, Products use occurencies by age 27206, And finally we can implement our models I applied all of them here at the same time and sorted the accuracy scores of the models in a dataframe You can implement it one by one 3184, Quite a lot I just drop everything create dummies because these models require that and obtain the following 3972, Stacking 23747, Make Predictions 4079, Like all the pretty girls SalePrice enjoys OverallQual 24512, Currently the sampler is a random sampler 13845, Missing Data 29956, Item Description 5688, Drop columns that are not required 36031, Build ensembler and make CV scores 21765, A few values in ind actividad cliente are missing 28871, Inverse Difference transform 23186, RF 749 makes exactly same correct predictions true positives true negatives as gbc 749 hence rf and gbc have exactly same accuracy score that we saw when we calculated both model s accuracy score 38028, separate input variables and target variable 38996, Image on the left Read and Resized Image 15241, Embarked 32573, We can visualize the learning rate by drawing 10000 samples from the distribution 1206, LightGBM 10699, Testing set 20047, there are outliers from data 7465, Using cross validation for more robust error measurement 28505, Compiling and Fitting the model 36880, Perceptron 3534, FactorPlot FirePlaceQC vs SalePrice 18709, create a new learner with data cleaned 24130, It s quiet evident that some words occured very less in tweets so we can remove these words from our corpus to decrease dimension of Bag of World model 14106, center Pair Plot center 36384, Predictions 42736, A client can have several loans so that merge with bureau data can explode the row of application train 13726, We know which columns to drop We drop them without further analysis 29364, LOGISTIC REGRESSION 8921, Preserve original train and test dataframe 33451, know try to identify best model 19561, Hydra config 9724, who is the winner 14272, Confusion Matrix 20597, Feature Relationships 23363, We perform a grayscale normalization to reduce the effect of illumination s differences Moreover the CNN converges faster on data than on 32347, Text lengths 43007, Data cleanning replace strange value in columns 35348, The linear model gives approx 15460, Ticket Number 17392, Pclass wise Survival probability 40651, Method 1 train on full and predict on test 35104, Resize Images 33671, Day font 253, Model and Accuracy 9727, It may be useful to review the other variables in context of the relationship between LotFrontage and sqrt scatterplot 36024, Model selection 30625, SibSp is a numerical variable 38056, Submission creating 21129, The situation is interesting 35803, Final stacked model 9273, MODEL FITTING 32429, Bonuses 20650, A standard model for document classi cation is to use an Embedding layer as input followed by a one dimensional convolutional neural network pooling layer and then a prediction output layer 6888, Percentages of who survived The men s survival is tragically a lot lower than women s 30767, Comparing base learners 37486, XGBoost 29132, Parameter tuning 7946, We define also a callback to check the model after every epoch with ModelCheckpoint 3374, And next give it the path to where the relevant data is in GCS and import your data 11702, Pytorch Source Code 18253, Data generator 36565, Stack Models 36579, App usage by age and gender 19396, Some helpers for visualization 20602, Check the Titles that were extracted 12883, It s a little more clear now that if you were alone or in a family greater than 4 your chances of survival were lower 5095, Once the entity set is created it is possible to generate new features using so called feature primitives A feature primitive is an operation applied to data to create a new feature Simple calculations can be stacked on top of each other to create complex features Feature primitives fall into two categories 10583, Visualizing AUC metrics 19057, As the dataset is huge we can test the model by just training with 100 samples from the train dataset 20962, Feature Scaling 37541, Here we are using SGD optimizer for both the models just the syntax is different 27483, We train once with a smaller learning rate to ensure convergence 14347, Machine Learning k Nearest Neighbors 5116, Variable Types 30687, Importe o arquivo test csv e atribua um nome nico hexadecimal para o H20Frame test hex 22933, Age is another simple feature to handle but we can defenitely find some special features from this 33678, Difference Year font 23515, There are 2 elements in the class 3209, Simple feature engineering 18012, Test set 23679, Decoder network 1312, Imputing missing values 43260, Separando os DataFrames 26996, Not a lot of contractions are known FastText knows none 38415, Are boosting machines better than simple ones 2135, target encoding always helps 1340, we iterate over Sex and Pclass to calculate guessed values of Age for the six combinations 35668, Removing outliers prevent our models performance from being affected by extreme values 26014, well said Everyone like to work with data when it is clean and no painstaking efforts needed to clean and transform it 5655, As there are about 20 of Age values with NaN instead of just filling them with the Mean or Mean based on their Age group we use GradientBoostingRegressor and LinearRegression to fill the missing values 35705, Then add this column from previous investigation to the dataset y curve fit gla 2 curve fit gla 1 x data curve fit gla 0 x data2 37320, Select the first layer kernel size parameter 23635, Glove Embeddings 19577, extract city form shops 32041, One of the cool properties of XGBoost is the built in function to plot feature importance If we want we can use semicolon to suppress the output other than the plot 21356, Fit Model 33763, Examine NaN Values 11891, SVM 36361, For use with xgboost we wrap it to get the right input and output 35129, Adding an additional feature that records the no 1770, There is one person on the boat deck in the T cabin and he is a 1st class passenger 23399, apply the typical post processing functions to the predictions 18776, let s first concatenate the train and test data in the same dataframe 11674, It looks like most passengers paid less than 100 for travelling with the Titanic 4240, We can now retrieve the set of best parameters identified and test our model using the best dictionary created during training Some of the parameters have been stored in the best dictionary numerically using indices therefore we need first to convert them back as strings before input them in our Random Forest 1000, have a look at the unique values of Cabin 41419, Save test set id features with scaling 26567, But SVD does not mirror the axes 31749, Additional information about the clusters 36216, Data Cleaning 11877, Pred ML Evaluation 6470, Tune the best non ensemble methods 12826, SibSp and Parch Analysis 14562, Age We can fill in the null values with the median for the most accuracy 42235, Skew of target column 14159, take a look at the correlation matrix to get a quick insight about the relationships between features 30660, Aftershocks ruins and body bags are the most fake topics in Twitter 22951, quickly visualize the data before wrapping up 20380, we only have text and target columns only 32081, Continuous Variable Summary Statistics 23927, read the predictions and make a submission 11649, Random forrest 7505, Missings count in the train test set 10362, Making Predictions and Submission 18080, Area of bounding boxes per image 3584, When I look at two situations it is similar from 25quantile to 75quantile so it may be ok to fill with median 38046, The 95 confidence interval defines a range of values that you can be 95 certain contains the population mean 17573, Gradient Boosting 38136, We now scale the data Scaling is used to reduce the effect of feature with higher magintude to take over a feature lower magnitude 36355, Generate Predictions 22431, There is one more way to create this types of plots and it is using the gridspec 14644, let s work on the label encoding and one hot encoding for the categorical features in our dataset 37210, Functions Embedding Related Functions 41706, This highlights the variance looking suspiciously like streets 42755, go more specifically into this function 8480, XGBRegressor 1249, The SalePrice is skewed to the right 8486, Robust Regressor 7766, Elastic Net Regression 6209, Decision Trees 13232, Data exploration 860, Sex Female more likely to survive than male 18409, Cross validation 10847, Looking at Skewed Features 41574, Label Encoding the Y array e Daisy 0 Rose 1 etc then One Hot Encoding 28210, Define the model and the metrics 41661, We decide to deal with missing values as follows 11839, We have successfully addressed all the categorical missing values move on to numerical missing values 664, Bagging 25997, Pandafy a Spark DataFrame 1664, There is a significant difference in the groups medians 12255, We need to standardize our data in order to properly use SGD and optimize quickly 13533, From my kernel 22057, Distribution of number of words per sentiment in train data 17791, We succesfully set passenger as a Mrs 17950, Train 22954, Model 30661, Which topics are the most controversional 35504, Predict For Random Sample 32189, mini batches 128 3936, Another checkpoint 16718, Model evaluation 38784, Create assumed probability distributions 17633, Family 23210, Stacking Or Stacked Generalization 15913, Title Survival 22624, we should create our own network and train it 1536, It s clear that the majority of people embarked in Southampton 20693, Develop Recurrent Neural Network Models 7752, it s time to select useful categorical features for our model 30085, Support Vector Machine SVM Algorithm 25652, Run the next code cell to train and evaluate a random forest model 20186, Descriptive Analysis 38619, Yeap there are the same descriptions withing the train data but with different date of creation 36288, Dateset is completely ready now 20784, Distribution of Data 14838, Age 22047, Validation Methodology 4900, submit our solutions 10740, Import the raw data and check data 10909, Compare the performances of the tuned algorithms on our dataset 1716, Importing Libraries 36993, Do people usually reorder the same previous ordered products 11013, Lets map Salutations to ordinal numbers 25402, ONE HOT ENCODING 16162, Testing 16450, We found something interesting Cherbourg port is very safe for females and Qweenstone and Southampton ports are very dangerous for males 26960, Monthly Sales and Revenue 23740, Feature Selection 23981, we load the required files 3576, Train vs Test 14567, We can drop Name and Ticket column 41587, I have tried to train the model from scratch 14137, Model building 21203, Large Learning Rate 17663, Fill missing values 37663, Data preprocessing 25422, Test and train date column comparaison 10630, RandomForestClassifier 1278, Filling Embarked NaN 37103, Recursive Feature Elimination 910, Ahh Showing linear relationship 4615, Is it possible to drop more columns and shorten running time even further 5100, The next step is to use linear models penalized with the L1 norml 17966, The usual string accessor methods now work can be used for data manipulation 9830, Adding New Features and Filling the missing values 16252, Plotting 31042, Boundaries 41411, Ther was a strange value 365243 it could mean empty values or some errors so I replace it with zero 8428, Check if all nulls of Garage features are inputed 35690, Training and Evaluation 10134, LightGBM Light Gradient Boosting 3894, Distribution plots 32549, Scaling of Data 4859, Load train and test data 10279, the model at least makes some logical sense 1529, Parch Feature 31092, GarageArea font 38181, Hyperopt 12721, It looks clear now that Embarked Cabin really aren t helping us out and therefore I am going to get rid of them 43351, In our yp e 36484, Code for Loading Embeddings 14627, Right then so now we know that most of people died the number of men are twice as many as the women most people belonged to the Ticket Class 3 did not travel with their siblings spouses parents children and embarked from Southampton 22461, Violin plot 36983, There are few variables at the top of this graph without any correlation values I guess they have only one unique value and hence no correlation value confirm the same 24296, Define Variables 36462, Close Analysis of a Single Image 23392, Wrap these post processing functions into one and output the predicted bounding boxes as a dictionary where the image id is the key 23080, Correlation matrix 1319, adding some additional features 8061, Heatmap 1663, It looks like there is a lot of explanatory power in Pclass To keep the analysis intuitive we use only Pclass to impute Age missing values since they have the highest correlation in absolute numbers 11714, RandomForest 7344, Make submission 17636, Sex mapping 15012, Option 2 is to use the Seaborn catplot which is a much faster way of visualizing the relationship between variables 29614, Univariate analysis 33204, Creating submission file 16050, Sex vs Survived 27403, Define the optimizer and loss functions 15995, Searching the best params for XGBoosting 19903, Grouping by Month Shop id and Item id 14664, Seperate Train and Test 32557, Source 14506, Imputing the missing values with mode 9940, Creating a feature with the titles of the name p 6751, Train set 20524, Another way to check for correlation between attributes is to use the 27998, Label Encoding 3319, Predict and submit 9711, Cross validation on Ridge regression 35100, events csv 32926, Random Forest model 4148, Imputation of Age variable 11150, LotFrontage Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood we can fill in missing values by the median LotFrontage of the neighborhood 14453, go to Correlation section corr corr 14607, Decision Tree 35435, Creating Train and test dataset 3809, XGBoost 15986, Naive Bayes 2432, Remember how we transformed the Sale Price by taking a log of all the prices Well now we need to change that back to the original scale 1017, let s start our hyperparameter adventure in the Random Forest 8751, Final Fit 29566, Number of binary values in row 24768, CatBoost 35184, Projection into 3 Dimensional PCA 14135, Creating dummy variables 18976, Display values in table format 35802, Residuals Plot Actual vs Predicted for Train data test for normality of residual errors 41307, Preliminary investigation 20289, There is around 55 chances of survival for those who have boarded from port C 2571, Gaussian Process Classifier 2171, Age is the next variable in the list 32959, For all numerical features mean value is approx and standard deviation is approx 29957, Create the model 10701, Encode Train Test 35580, A simple submission 295, Missing values 39265, order feature ABSOLUTE TIME 16250, File Paths 32269, Go to TOC 29688, Loss Function which calculates the difference between current output and actual output Here CrossEntropyLoss is used which is commonly used for Multi class Classification problems 7801, Iteration 2 Setup with Preprocessing 42651, Build and train BERT model 24889, And time to scale features to get their values as less as possible 4695, After these log1p transformation most of our features have a smaller skewness 2204, Making several new features based on the size of the family 12355, BsmtFinSF2 Type 2 finished square feet 20429, Importing necessary modules 14725, We can expect that the one guy who paid way more for this than everybody else did survive 677, Ranking of models I ve borrowed that one straight from this very nice kernel because it s a useful summary display of how our models perform 14525, Observations 35836, Checking Duplicates Rows 27148, Neighborhood Physical locations within Ames city limits 33456, Correlation 12023, Feature importance 29821, Pre Trained Word2Vec 26848, Fourth batch 12646, Random Forest 35318, CNN 5934, Applying box cox1 transformation 2363, Introduction to Receiver Operating Characteristic curve ROC 30856, Predicting 5315, Linear model fitting using Scikit learn 18848, FastAI Tabular Learner 18274, LOGISTIC REGRESSION TO FIND HYPERPARAMETER 27433, FINAL DATA 39254, Import data 6616, One Hot Encoding 13079, Feature Selection 15993, Best Model 27304, How does the total number of products owned across all accounts evolve over time 23800, One hot Encoding 38997, Reshape the input x train dev to a vector e currently x train dev is of shape number of examples image width image height number of color channels 8448, Transform Years to Ages and Create Flags to New and Remod 17029, Outliers can shift decision boundry for linear models significanlty thats why is it inportant to handle them 10210, Survived is a target variable where survival is predicted in binanry format e 0 for Not Survived and 1 for Survived 5432, Garages 19270, LSTM on train and validation 12739, Final model prediction submission 36246, Statistical Significance 28493, Use test subset for early stopping criterion 38484, Create model with TPU font 42085, Lets create a data object using really cool fastai API 40998, Conv2d layer takes 3 input channels and generate 64 filters channels e feature maps 24161, Build Train Set 15297, Gaussian Naive Ba Model 1059, Nice The most important feature is the new feature we created TotalArea 11983, let s check the scales of all numerical features to verify is there any scaling required or not 23421, Number of characters in tweets 23058, to make our life little bit easier we transform our dataframe to have only two columns label and image where image is a numpy array of pixels 4688, Again here i set these missing values with the most common values 38216, Train 28873, we return our Selected time series into a data frame 17634, IsAlone 15939, Test Fare 38509, create three separate dataframes for positive neutral and negative sentiments 6658, Prediction Classification Algorithms 11914, creating different age bands 32589, To save the Trials object so it can be read in later for more training we can use the json format 14437, go to top of section eda 6620, Bagging Regressor 31118, delete duplicated features 24691, As we are interested to finetune the model to APTOS19 we replace the classification fully connected layer 4408, Variable transformations 34519, Applying Featuretools 36751, Here is again an important part 37186, Reading dataset 39388, Fill null values for cod prov with median 8034, Since it s catergorical datatype we opt for Mode 8396, Ok now let s set our X and y values 14850, Cabin and Ticket 41865, Below I genrate a wordcloud from raw text excluding stopwords and in a shape of fire 31395, that our data looks good lets get ready to build our models 24241, Completing features 40485, AdaBoost 37097, Depending on the categorical variable missing value can means None or Not Available 2490, so there are some misspelled Initials like Mlle or Mme that stand for Miss 17766, Go to top font 10557, Merge Train and Test to evaluate ranges and missing values 517, some useful functions 2037, We change Sex to binary as either 1 for female or 0 for male 18293, Making a Synonym Dictionary 2543, The principal changements are here with the lines beginning by for categorical features 8232, Model Building 14792, Pipeline 2760, Observations 39300, Discard irrelevant features 23400, Although only a submission to the competition provide a final score on how good the model is I ll visualise each test image with their predicted boxes to get an idea of the models quality 37786, Visualizing sample of the testing dataset 16771, Our random forest model predicts the same as before 24431, Building the CNN Model 29160, Fence Fill with None 31934, Submission 22659, Training Function 3281, Ridge Regression 29521, font size 3 style font family Futura color green Data Cleaned 43362, Random Forest 24759, Lasso Regression L1 regularisation 24566, Total number of products by age 2650, SibSp of siblings spouses aboard the Titanic of passenger 20572, Many passensgers are of age 15 40 yrs 32184, Train model 32804, LightGBM 13709, PRIMARY CONCLUSIONS DERIVED 3692, Exploratory Data Analysis EDA 42373, Word2vec Embeddings 5439, i 2 Calculate the purity score 28955, make a new data frame which contains all of the people info and add to it the profit info 16496, Random Forest 9419, Making predictions and outputting 11705, Check the Data 43374, Defining the accuracy function 27311, Model Architecture 13693, up we encode all the categorical features 38855, Submission 1972, Linear Discriminant Analysis 42768, Fare Group and Fare Categorie 21252, Compiling Model 33343, From our very first and simple figure we can already extract very useful information 30910, LAR1 may refer to Los Angle R 1 zoning 6177, Count of males and females aboard the titanic 30089, Evaluation Classification Models 22938, One last interesting feature I can create is from the documentation from the data 27044, Distribution of Ages w r t gender 931, Optimize XGboost Regressor 16402, We use Imputer from sklearn for the median values 27434, MODEL DEVELOPMENT 13103, We need to impute values in Age 17683, EMBARKED SURVIVAL PERCENTAGE 19647, Age distributions by phone model 11728, Linear Discriminant Analysis 37328, Pool size Parameter selection of New Module pool layer 6159, Feature importances 26766, Submit To Kaggle 1397, SibSp vs Survived 8920, Correlation Matrix 20848, We re going to run on a sample 37182, now try to predict using random guess but stratified using dummy classifier 15124, Sex 43041, Looks like we have hit our target of 99 14499, Numerical Features PassengerId Age Fare SibSp Parch 3225, Scatter Plot 26986, Compile model 7872, To explore better the relationship between these variables before featuring I create a first model 7222, Fireplace quality Null Values 19947, Feature engineering 32013, Now we can have a look at our train X and test X again 23660, Rolling Average Sales vs Time Wisconsin 12400, One hot encoding of all purely categorical columns 41191, The list of numerical and categorical columns might have changed after removing those columns 13689, Fare 40712, Loading test csv 16089, Embarked vs Survived 1650, Quite informative well apart from the NaNs but a good idea here would be to examine each feature separately given that they are not too many 29812, Displaying FastText pretrained WordVector of a word 35555, Boosting 17746, all tickets starting with were priced at for passenger s traveling alone I set the passengers fare as 14721, This one is a bit more interesting 25171, Pre processing our Questions data Removing Stop Words Doing Stemming and more 10409, Plot sales price against Living Area sliced by Overall Quality 12887, Name 12840, Linear Support Vector Machine 23505, Compile and run the model 4528, Series Pandas 15077, Correlation 42332, Dropout function used to avoid overfiting of model by terminating disabling random node while building 12021, Nice It also performs similar to xgboost 7740, Predict 11301, Averaging 30266, We can use custom threshold value to fine tune our classifier in precision recall space 26577, Training the model 31256, Making Prediction 26883, Include only numerical columns impute columns with missing values plus an extension to imputation 18994, Build the neural network 16882, People who are alone aboard have low survival rate 2168, Our assumption is that people s title influences how they are treated 31900, Scale these values to a range of 0 to 1 before feeding them to the neural network model 41754, Approach 2 pd read csv and pd to sql chunk by chunk 26291, Predictions 7929, Ridge model 38716, FCGAN Implementation 28890, As a structured data problem we necessarily have to go through all the cleaning and feature engineering even though we re using a neural network 11741, we have a much better feel for a lot of the variables that have the largest impact on the Sale Price 3292, we need to create the Embarked Kfold Target Enc in the test dataset by using the following class 43204, Prediction 28503, Creating Label 37509, View data for a single customer 10169, Count Plots 5528, Create Age Band Categories 14884, we can get all the types of Cabin are starting with these character 11199, BayesianRidge 24713, Show Model Variations around the Mean Image 9635, Checking out the number of Categorical Data and Numerical data and adding them up to find out the total feature types 24293, reshape it to 28x28 12388, Plotting the correlational matrix 7675, We dealt already with small missing values or values that can t be filled with 0 such as Garage year built 40416, Looks very similar to the train set dates and so we are good to go 26009, There are also other significant variables that I missed during the earlier sneakpeak and that s the beauty of EDA They are 13692, We begin by dropping the columns that we are not using 3238, Maps 13990, Drop Ticket PassengerId and Cabin columns 26460, GB Prediction for test dataset 22162, Pipeline taxes 21788, Var21 var36 32313, Relation between Survival and Family Members On board 21609, Apply a mappings or functions to the whole df applymap 7300, Bivariate Analysis 4716, Adding certain columns 741, Overfit Columns 35374, Load Model 41362, There is no correlation 10390, Preparing the Data to do Machine Learning 25385, Visualize the output of fully connected layer 11045, And now use our pipeline 37427, Which are the most common stopwords font 9731, Model Building and Evaluation 34627, EDA Feature Engineering 23893, is the mean value with which we replaced the Null values 7546, logistic Regression 15728, Hyperparameter Tuning 23692, Define transforms 16227, as we have applied all the feature engineering steps so now its time to separate our data back 29152, FireplaceQU Drop Feature 10973, Top influencers 41966, Sales Per Month Count 42658, The data set is characterized by a large proportion of missing values 888, KNN KNeighborsClassifier 25727, check our datasets 41072, Misspelled data 16257, Preprocessing 19355, Numerical columns within the dataset 18330, DISTRIBUTION OF TARGET VARIABLE 9221, Execution on Testset with KNN 27013, How is ResNet50 working 8800, lets impute the missing value 15116, we load the test set to the variable X test 6147, Features encoding 11085, ensemble 30, Stacking 20554, Function monitors and changes the learning rate 4594, GarageCond and GarageQual are highly correlated 37812, Tweets missing the keyword location 28010, Confusion Matrix 32142, How to rank items in a multidimensional array using numpy 14697, PassengerId can be removed from the dataset because it does not add any useful information in predicting a passenger s survival 28711, Take training data through graph and evaluate performance 19587, shop 35666, Useless features in predicting SalePrice 4136, define some helper functions which would be used repeatedly 14427, Title Description notes 36343, Compute the Network Error 17448, first split in x and y values 17831, Model with Sex Age Pclass Fare Parch SibSp FamilySize Title features 21787, Var3 var15 var38 4202, Modeling 7386, By manually inspecting each unmatched name from the Kaggle dataset and looking for it in the Wikipedia dataset I discovered several matching mistakes 21648, Select multiple rows and columns with loc 39850, Actual value vs Modelled Value 20071, Insights 21461, Moving to the second chapter 14709, LINEAR SUPPORT VECTOR CLASSIFIER 7044, Proximity to main road or railroad 41575, Splitting into Training and Validation Sets 18317, any useful words in shop names 16037, Thats accuracy of our model 21181, Preventing Infections 17783, Extract Title from Name 19875, Feature Scaling 4871, Generating Dummies 22094, Define Loss Function and Optimizer 41964, Item Category 25945, EDA and Feature Engneering 35350, now choose the best hyperparameters 12365, Exterior Variables 7094, Basic Modeling Evaluation 18332, Just for demonstratio purpose the positive skewness in the data can be mitigated by using a log transform 16727, title 19087, Age histogram based on Embarked Survived 8781, One hot encoding for sex 14266, Decision Tree 41244, Tensorflow Hub Universal Sentence Encoder LightGBM 6378, Find out variance 38456, Go through all questions and records entity type of all words 38292, Define GridSearchCV for hypter parameter tuning 709, use a random forest as this should partially remove the dependancy on skews that we have with linear regression based modelling 13456, Cabin Missing Values 40943, Out of Fold Predictions 27741, There is no missing data 13959, Data type of each column 7926, Modeling 34674, Sales distributions by shop id 16831, An ROC curve demonstrates several things 16707, We can also store the values of each prediction model in a dictionary 40456, NeighborhoodHouseStyle 718, At this point I d thought it would be wise to try a different modelling technique 30402, Defining a class to get access about information after each update 29594, We apply the initialization by using the model s apply method 32180, Define Centernet model 17452, ok lets make some c and epsilon tests 31025, Mention font 28350, Analysis Based on EXter Source Types 20193, Lavene s Test 36212, Preparing Evaluating Submissions 26965, which shop is the most popular and lowest 24706, Inference on test set 24718, Show Scatter plot of Leaf images as points in high dimentional space 1940, Overall Quality 23244, Many machine learning models allow some randomness in model training 17761, explain mean mode median 11552, Effect of year on SalePrice 8242, Model performance Visualization 40130, Function to build a model based on LeNet 5 architecture 28302, There are 116 categories with non alphanumeric values most of the machine learning algorithms doesn t work with alpha numeric values 8464, Like before I excluded one by one of the features with the highest P value and run again until get only P values up to 0 22074, We got between 15 and 40 EMPTY PREDICTIONS That s the core reason why spaCy NER model does not perform well on this task We can t score over 66 X on the Leaderboard so far 27472, Top Ngrams I am analysing Bigrams only 34059, Outlier Detection 24868, Fortunately the imbalance isn t too bad for this dataset 6300, Extra Trees reduction 35138, Final Check 14345, Data Visualisation 21730, check out the outliers instead 22485, Bonus1 how to make simple lines to connect points in matplotlib 33656, Drop Unnecessary Columns 28173, Dependency Parsing 10126, There 4 graphs again reiterate the same fact in much more detail 27666, 3D CNN 3764, Lasso Regression 19468, let s check at the occurence of each class to be sure that there is no asymmetry in our data that can skew the algorithm 12064, Target Variable 7308, Observation 13790, Encoding 43135, Overall Model Performance 7860, We tweak the pre processing function from before to handle missing data better too 15653, LinearSVC 35500, Epochs and Batch Size 31782, Exploring correlation of features between the train set and target 26789, Naive variables 28113, The Model 8442, Include pool in the Miscellaneous features 12873, There are total 2017 parameters in our model which we tune while back propagation 33740, Lets plot some of our prediction 31259, start optimization process 41696, Exploring places and times 10498, Pclass 37947, If we want to predict a data point in the future currently only Country State and the date are known 3937, The sales price is right skewed 36166, Instead of simple removing duplicates I m taking into account only the last month for each customer 41053, Creation of X features and y targets based on the data sets 20280, Lets visualize this 34333, Prediction 28742, Trainning the model with best parameters and predicting the X test to submission 33582, Test Dataset 7217, We can replace the missing Alley values with value None 16001, Embarked 28488, We just reduced the dataframe size from 57MB to 35MB 4085, Univariate analysis 13510, Score 9751, Re check for missing data 33798, The target 1 curve skews towards the younger end of the range 4024, Electrical 17785, verify the relationship between Title and Sex 24969, V11 prediction 21430, Explore NaN Values 20526, Prepare the Data for Machine Learning Algorithms 3480, Make predictions on the training set and construct a confusion matrix 6021, Ini adalah data submission kita 20452, POS CASH balance 16671, Model Tuning 33833, Replacing With Mean Median Mode 23545, Set the optimizer and annealer 15606, Estimate missing Fare Data based on Embarkation 18068, Using the Model 8513, Fixing Skewness 4824, Plot the distribution of missing values 27596, Simple LSTM Model 11652, Adaboost 17930, Explore 10697, SibSp and Parch processing 36744, 1 is assigned the day before an event exist 2468, Random Forest Importance 2348, First Find Ideal Boosting Rounds 33150, MEMO If you are predicting on your own moles your don t need to rank the probability 26043, we ll define a DataLoader for each of the training validation test sets 21237, We can use callbacks to stop training when there are no improvements in our validation set predictions and this stops overfitting 24179, Stacking 16919, Confirm features of train test are the same 6560, Pclass 24869, Imputing is the process of dealing with missing values 27916, Impute by Strategy 26957, there is an outliner in the item price and item cnt day columns 35858, now we reduce the learning rate by a lot and train the model for real 35672, Numerical features 33796, As the client gets older there is a negative linear relationship with the target meaning that as clients get older they tend to repay their loans on time more often 29791, Euclidean Distance 442, MSSubClass Na most likely means No building class We can replace missing values with None 36298, Look Accuracy on Training data lol 8050, feature engineering 5599, Bath 38506, Text Data Preprocessing 27995, CatBoostClassifier 1606, Age 31670, Define Helper Functions 4426, Some features are very highly skewed and this can negatively impact the model 12102, Dealing with LotFrontage 36595, Main part load train pred and blend 3337, think more logically and find certain features which have significant impact on missing data lets start corelations and find which features are similar to Age 36846, when tuning a model it makes little sense to only track the change in CV score We have to tune models on a local test set in order to get a valid estimate of how well it perform on the leaderboard 3189, Follow the documentation 31737, DICOM Images 2664, there are 38 constant feature columns with same value in all the data out of 370 columns 15171, No survivors 21926, Test Score 12284 42959, It s clear that the majority of people embarked in Southampton 28308, Function for find out Numerical and categeical Variables 28079, we come to imputing missing age in the test data 16754, SibSP Parch 32521, Predictions 32657, Visualization of distribution and correlation with the dependent variable AFTER normalization p 4512, Considering number of missing values and its relationship with LotArea we ll drop it 814, Missing values in train data 1804, let s make sure that the target variable follows a normal distribution 12220, There is a big outlier in our prediction and a visible pattern in the residual plot both things that would require further investigation 28797, Training models for Positive and Negative tweets 16944, Tuned Random Forest 29465, Frequency of each question 13593, Build AdaBoost 29435, Importing Packages and our dataset 39021, Columns dissociation 19160, Basic feature engineering 23086, Here we can do an evaluation of our model 27164, Line plot that tells us the variation of each year with Sale Price 32520, Loading the weights 33891, agregating installments payments features into previous application dataset 17646, Logistic Regression 2562, Variable importance cross validation details and model stats 20810, We select every numerical column from X and the categorical columns with unique values under 30 10643, Methods to deal with Continuous Variables ways deal continuous variables predictive modeling 440, Functional data description says NA means typical 14890, Embarked 32686, The python list containing the loaded data is converted into two numpy arrays one for features and one for labels 19672, Remove Low Importance Features 3562, The std is big 23901, let s pad the sequence to fixed length 21326, Pools Hot tubs 14240, Most of the first class passenger embarked on S 1121, Based on my assessment of the missing values in the dataset I ll make the following changes to the data 17762, Drop Useless Columns 11838, After careful examination of Utilities variable we can drop it 1661, A clever idea at this point is to ask our friend seaborn to provide a features correlation matrix 27915, One hot encode the categorical features and identify features that are failed to encode 15137, Cleaning 11523, LightGBM scores 27225, Creating a intermediate layer model to extract the data from my dense layer 14582, The diagram is rightly skewed 17760, Missing Ratio of Columns 37922, Ridge 3064, Filling in the NaN values 900, For delailed variable description please check out here prices advanced regression techniques data 12349, BsmtFinType1 Rating of basement finished area 42955, The number of people with two or more children or parents is very low in the data 17371, Embarked S 16436, Fare 11647, Naive Bayes 38708, I get rid of some features for best LB score 19093, that s pretting amazing correlation 4075, Final Submission 494, Embarked Feature 28651, Location 14457, go to top of section model 38960, As mentioned in the introduction we are using the default here 18995, Do the train val split 36054, Test Data 8544, FireplaceQu 1174, let s think about imputing the missing values in the numerical features 4470, We create a column for each cabin and insert the value 1 if the passenger belongs to that cabin and 0 if the passenger do not belong to it We only create columns for cabin A B C D E F G T and LEFT OUT CABIN U in the columns created in order to prevent collinearity Passengers in Cabin U would have values 0 for all the cabins columns A B C D E F G T 7893, As Sex and Embarked are not numerical I do the pandas OneHotEncoder 26358, Model Building Baseline Validation Performance 1052, Machine Learning 2274, Creating new Features 18460, preprocess the question text 12656, Splitting up the Training Data 14062, Sex vs survived 31320, Decision Tree 13616, we fit clf cat to training dataset and apply it to transform 34409, Save as CSV 23754, A little editing with the datasets for Analyses and Prediction 6542, numerical columns 35157, Plot the model s performance 16983, Nearest neighbor classifier 6198, Decision Tree 11421, Find 532, Swarm and Violin plots 22512, comes the cool part 20101, Item count by month for 1 2 3 6 12 lag 38447, Convert categorical columns in numerical dtype to object type Convertnumericalcategoricalobject 21999, Highly compressed 9874, We can fill Embarked data by the help of passengers class or by looking fare data and which seaport the passangers prefer to enter the ship 8829, FINAL DATA PREPARATION 42011, Making a new empty DataFrame 2538, To use Tensorflow we need to transform our data in a special format 6295, Voting 32615, Exploring the keyword column 31768, Here are there order counts 6174, Shows the count of survived people 728, A strong score 27260, look at the correlation between the regressors 3905, there is one missed column which is the y we want to predict 32799, Random Forest 5535, Create Parch Categories 32975, Lets fill the missing Ticket value in train data with median Ticket value and one missing fare value in test data with median fare in train 32866, Lag based features 33532, Not much difference in distribution of average words length in tweets with target 1 and target 0 26368, Images that confuse the network 26904, Approach 10 A10 16432, Decision Tree 12122, Alley data description says NA means no alley access 14571, Random Forest font 24409, Feature Importance 5988, Gabungan Data Train dan Test 4192, Important categorical variables MSSubClass and Neighborhood 7723, In these numerical features we ll impute NaN with zero because a missing values here means the house doesn t have that feature so it s zero 16664, Binning Continuous Features 19620, DATA PRE PROCESSING 29079, Price 27037, Exploring the Target column 4089, SalePrice is not normal 11484, MasVnrArea MasVnrType 6190, Feature Scaling 5199, now we get the label from the training set and put it in a seperate series then we drop it from the training set for future use 23200, Not so much But considering the number of features we have its not either too less visualize our two components transformed features in a scatter plot 13907, Roc Curve 10145, Picking the right model 184, Label Encoding 10575, We using PysparkDataFrame na fill to fill a value to specific column 32839, Most of the plots for ConfirmedCases and Fatalities look like a degree 2 or sometimes a degree 4 polynomial 30903, analyse the regionidneighborhood 5923, Learning from others kernels 20270, There are 116 categories with non alphanumeric values most of the machine learning algorithms doesn t work with alpha numeric values 1573, We combine the SibSp and Parch columns into a new variable that indicates family size and group the family size variable into three categories 41719, Import and load data 6793, Ramdom Forest 26473, In this example we be using the pretrained for fine tuning by doing the following 9695, EDA on Categorical features 36615, Calculate Eigen values and eigen vectors 38581, when ever we have a text data we have to clean the data for remove some unnecessary symboles and uncesessary stopwords 22432, But if you want to make not a regular n x n column but something more sophisticated you can only do it using grid 28869, Predict 14085, Test Data 18440, Setup the model font div 38554, Correlation Matrix 12101, Dealing with MSZoning 2011, Feature Transformation Engineering 39144, look at train 28643, OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch 2812, Reading the data 32316, in order to run SelectKBest I have converted values in Embarked and Sex to floats 3639, Treating Mising Values before performing further Feature Engineering 13368, Here 0 stands for not survived and 1 stands for survived 636, In the same way having a large family appears to be not good for survival 3974, Submission 5554, Submit entry 13966, Embarked 28307, Number of records and Features in the datasets 33786, Label Encoding and One Hot Encoding 22709, Splitting the target and predictor variables 9817, As predicted females have a much higher chance of survival than males 19726, Observation 29163, MSZoning Fill with the most frequent class 39124, With XGBClassifier 27171, There are two types of popular feature scaling methods 18585, Here is how the raw data looks like 37422, But our task is not to predict these labels but to predict the selected text which can help us figure out the sentiment 15459, Price distribution for Pclass 37879, Prediction from Linear Model 32969, Deck 16835, Using sklearn utilities for the same 21641, Copy data from Excel into pandas quick read clipboard 6905, Port of Embarkation 34035, Ridge regression by increasing alpha 26042, To double check we ve correctly replaced the training transforms we can view the same set of images and notice how they re more central and have a more standard orientation 15523, We can now get the prediction 28866, Decoder 18250, Unique values in dataset 36788, Bigram Models 4573, Fourth step A little bit of Feature Eng 536, New Feature NameLenBin 8954, Fixing Miscellaneous 40081, we fit the test 25734, In this Problem we used softmax except sigmoid 882, fillna fill nan with mean values for that column 11053, ENSEMBLE Weighted Voting classifier p 20586, Logistic Regression 5040, As expected the skewness is a positive value 23461, Removing unnecessary columns 34460, WI 2 1313, Fix Skewed features 42420, Finding Top Contributing features 27511, Extract our features and target 15101, For our next step we are going to assume that their is a relationship between a person s age and their title since it makes sense that someone that is younger is more likely to be a titled a Miss vs a Mrs 26524, Visualize Logistic Regression Predictions that are MOST wrong with ELI5 22618, Plot the distribution of apps on the 75 of the devices 22803, Few Data Observations 2505, Family Size and Alone 37610, return missing col is a helper function to find the missing columns of a dataset easily 16022, We remove Z score 3 thats mean Fare 200 11720, Linear Support Vector Classification 19337, There they are bring back the single unconnected Questions to get our complete universe of Question Sets 22704, Network Structure 20257, Basic Math with Pytorch 31058, Format Country code Local Area Code Number 22450, Dot plot 11191, This model needs improvement run cross validate on it 12950, Stacked Classifier 41618, lets set up our plot again 9159, Correlation between Target Variable and Features 1400, Sex vs Survived 23390, Prediction post processing 13667, Random Forest 26620, Data exploration 13909, Number of males that survived vs number of males who did not survive 13541, First look at the data 17734, Looking for NaN s 39734, While I m at it I ll encode it and Sex as numeric using the map method 11815, visualise this data in in boxenplot violinplot and stripplot 36627, How many buildings and managers 16546, use the get dummy method of pandas to encode the Embarked column 23903, Get the sequence embedding 7027, Home functionality 41988, isnull sum isna sum To check the sum counts of null values 21769, Drop nomprov column 19637, 529 device ids have duplicate entries in phone dataframe 26260, Plotting the training vs testing accuracy 37199, This model may be very sensitive to outliers 26934, And now we can obtain the features matrices 6481, Check Missing or Null Values in data 13364, View statistical properties of test set 36098, Save some memory 41567, n components means to what dimensions you want to reduce the dataset to 6091, GarageCars and GarageArea are like twins 5705, BsmtQual BsmtCond BsmtExposure BsmtFinType1 and BsmtFinType2 For all these categorical basement related features NaN means that there is no basement 42081, we need some labels too so lets add y which be our label 23996, Here Again train and test are spilt back seperately as now all data processing is done 11706, Data Cleaning 31123, let s look at the top xgb chosen features 2387, Most important parameters of a LogisticRegression 12043, understand a data set variable after variable check basic statistics and drop a few outliers 36297, Magic Weapon2 Support Vector Machine with RBF kernel 9686, Observations 10571, EDA in Pyspark 27043, Visualising Age KDEs 29417, All the functions are below and quiet basic 23180, Findings Among the classifiers RF and GBC have the highest accuracy after tunning hyperparameters RF and GBC are perhaps worthy of further study on this classification problem Hence we choose RF and GBC 27524, Library 24687, compare the number of parameters with some of ResNets 2214, ROC 20785, Univariate Analysis 24366, majority of them are numerical variables with 15 factor variables and 1 date variable 7121, Classes of some categorical variables 30887, We now have fixed all parameter values but searched only coarsely for the best number of estimators 33777, Data Augmentation 2999, Trying some nifty Feature Selection Techniques 27078, Thus we have our very high rank and sparse training data small document term matrix and can now actually implement a clustering algorithm Our choice be either Latent Semantic Analysis or Latent Dirichilet Allocation Both take our document term matrix as input and yield an n N topic matrix as output where N is the number of topic categories which we supply as a parameter For the moment we shall take this to be 5 like categories number 3613, Independent variables 31292, Separating macro data depending on its period 39846, Actual value vs Modelled Value 23736, The code may look a little overwhelming but if you look in between the lines it simply extracts the title as it is formatted in the name column 7629, Linear Models 16543, As there is only one missing value we can easily fill it up by either mean or median of the column I am using mean here 21105, Our date block num column be the sequnce index sales be the value 9904, Train Test Split 22358, Frequency Encoding Count Encoding 1981, let s check that heat map again 40258, Ground Living Area 12877, Cool 37460, Remove Stopwords 20637, Unigrams 14538, Summary 34428, Real Disaster 18201, let s look at which proucts sell by week with interactive heatmaps use our quantiles here 28108, Combining the highly correlated columns based on the distribution 7870, To get a better insight of the relationship of these features and the survival rate a general pairplot give some clues 25085, Event 2 vs sales FOODS 41976, To read bottom 10 lines 34724, Feature importances from LOFO 30880, Plot crime categories counted per year 19315, Evaluation prediction and analysis 20619, Gaussian NB Classifier 6736, SalePrice vs MasVnrArea 3934, Replace lacking values with most common or median 12969, Filling the missing values in Age variable 964, Correlation Heatmap of the Second Level Training set 31702, Transforming the Data 23435, Even if I m not good at spelling I can correct it with python I use pyspellcheker to do that 11950, Applying Standard Scaler to the numerical dataset 2234, Kurtosis 14972, Deleting columns which are of no use 36499, Outlier Detection 29156, BsmtQual BsmtCond BsmtExposure BsmtFinType1 and BsmtFinType2 Fill with None 4406, Permutation Importance 18018, Boy feature 9176, GarageFinish 24019, Train all 14906, Embarked S and C have higher survival rate than Embarked Q 9949, Bar graphs and CountPlots 31998, get the test set predictions as well and save them 26108, Helper Functions 9636, Checking out the shape of our training and testing dataset 21039, Topic Probability 33449, ExtraTreesClassifier 41748, MLP Batch Norm on hidden Layers AdamOptimizer 2 24842, Complex CNN 25812, Confusion Matrix 38405, Sklearn models 24947, Selection by modeling 33673, Week Number font 26402, We already mentioned that most likley certain age groups might have a higher probability to survive 42531, One Thing I have noticed that the curve of a countries confirmed cases and fatalities looks pretty same And it make sense if you think if the confirmed cases fluctuates deaths would also increase or decrease 23517, Data cleaning preprosessing 41434, Brand Name 33361, The previous plot is kinda nice but we can do better 36509, Embarked Sex Plcass Survived 18211, Os dados est o em valores entre 0 e 255 20241, I have used two types of Imputer from sklearn 27384, the next cell take a lot of time you can skip it and run the one after it 28628, GarageType 22329, Removing Digits 10929, Undirected Network 32978, Machine Learning 597, Passengers with more than 3 children parents on board had low survival chances 16129, Covert Pandas Dataframe to H2O Frame 33274, Cutout data augmentation 2020, Here we stack the models to average their scores 38825, Split train valid 80 20 7296, Creating Submission File 25, RandomizedSearchCV Kernel Ridge 6619, Xgboost 27201, Fare 20814, Our next step is setting up the final model 5898, Label Encoder of Train and test 12486, Gradient Boosting Classifier 1235, Removing overfit 14327, Name 35079, The final submission is done using the model that yielded in the best score in this notebook that is the model 4 2 I just decided to compile it using Adam as optimizer because it finds local minima faster 31297, Compile Our Transfer Learning Model 11560, Apparently the best model is XGBoost let s plot a boxplot to visualize the performances of the different models 43324, take a look at our data 20633, Lets create our own vocabulary 6105, combine two datasets and work with missing values faster 18221, Training Function 17549, lets analyze the available Age data 41946, We can visualize the training process by combining the sample images generated after each epoch into a video using OpenC 3424, let s put all the prefixes with only one member into a single category called Unique 4471, Dealing with Embarked Missing values 30611, Over half of the top 100 features were made by us That should give us confidence that all the hard work we did was worthwhile 5986, Data Train 8496, SHAP values are calculated using the library which can be installed easily from PyPI or conda 38686, Before Mean Encoding 13324, Wrangle cleanse and Prepare Data for Consumption div 27469, Stopwords Removal 41680, Extract features from Images 27906, Reference We used Decision Tree Regressor A cool video on Decision Trees 21678, The n components variable here is crucial 22408, By now you ve probably noticed that this number keeps popping up 22846, Merging Shops Items Categories dataframes to add the city label category id main category and sub category feature 6629, Most of the people who died were from Passenger Class 3 irrespective of Gender 35371, Create train and valid dataloaders 556, KNN 4738, We ll consider that when more than 20 to 30 of the data is missing we should delete the corresponding variable and pretend it never existed but we do it in feature engineering part 1145, We have to convert all columns into numeric or categorical data 5529, Gender Categories 17259, Info of training data 2551, We have 5 categories cut off from 16 then at 32 48 64 3673, Optional Box plot 15467, Features SibSp and Parch 19433, Create a correlation matrix to check linearly highly correlated numeric float variables 30137, Creating Custom Model 5658, Basic info of Train data 14663, Standardize 4651, Basic Statistics 41124, Median of Absolute Logerror 7238, Data Preparation 0 35942, GradientBoosting 8607, We can then perform box cox transformation on the independent variables as well 2759, Numerical values distribution 22693, feature X contains 8 pixels 784 28 28 101, Train info 14717, RANDOM FOREST 23560, These have their kitchen area larger than the total area of the house 27570, ps ind 14 41703, Nope not really 17030, All features have outliers 7734, Tutorials for Models 3922, this is a tricky function to remove outliers I have searched a inbetween various methods but this was the fantastic one 28312, identifying the missing values 9979, relationship between these discrete features and Sale Price 27892, Split the train data into train having 20 000 images and test having 5 000 images 25396, Miss labeled data visualization 26451, knowing that our model is already quite good we are ready to make the final predictions 38711, Dimension Reduction PCA 32421, AlexNet 20090, Outlier 37490, LSTM 34698, Averaging the values based on the extrapolation over 1 2 and 11 months 13414, Specificity True Negative Rate 12018, XGboost is one of the state of the art model which is a faster implementation of gradient boosting with slight modification 8053, single model 16839, Handling Missing Values 43035, One Hot Encode the Labels 1922, Having 2 fireplaces increases house price and fireplace of Excellent quality is a big plus 41062, Predict Test 22654, Threshold 2052, DecisionTree Model 4670, The train set is composed of 81 columns so we not explore all features but focus on most important ones 31225, var 68 var 91 var 103 var 148 and var 161 have comparatively lower range of values 5959, Pclass and Survived 36626, Prediction Submission 31512, Feature Selection using Feature Importance 16672, KNN 15277, After saving the training and testing dataset in train data and test data dataframes repectively we ll replace the missing values in Age and Fare columns with the average values of the respective columns 38060, Cleaning stage 22628, Making Predictions on Test data 6938, Final set of features 111, ticket 32779, Visualizing Augmentation 27230, Prediction for res 29765, Validation accuracy class 34607, Cross Validation 1221, Splitting the data into categorial and numerical features 6740, Observed Outliers and non linear relationship 10388, Visulizing the Distribution of SalePrice 4753, its told us that OverallQual have good relation with sale price when quality increases sale price exponentially increases and also have some good relation in FullBath TotRMSAdvGrd GarageCars with sale price as well 1131, Exploration of Traveling Alone vs With Family 26095, Generate bonus output 23677, Model construction 23931, How can we use Faster RCNN in this competition span 18714, Trick to create an even better model 15480, We create a new feature Age Class to increase the number of features 24961, Feature Scaling 37536, Reading the data 431, FireplaceQu data description says NA means no fireplace 42963, we ll fill in the missing values in the Age feature 35229, How punctuations play part in this competition 16879, The age distribution between Survivors and victims are not very different 5687, Normalize Age and Fare 18071, Construct dataframe with all images 2237, Dealing with Missing Values 40921, Here s what the fit looks like in sample 13282, In pattern recognition the k Nearest Neighbors algorithm or k NN for short is a non parametric method used for classification and regression A sample is classified by a majority vote of its neighbors with the sample being assigned to the class most common among its k nearest neighbors k is a positive integer typically small Reference Wikipedia nearest neighbors algorithm 17435, Name 5605, TransFormation 12530, Spliting back the train and test data 5665, Create Name Length 2552, explore gender wise survival too though we use column as it is 40627, Just to sanity check let s have a look at the predictions on the training data to check they look ok and are approximately on the right scale 41468, It appears those that embarked in location C 1 had the highest rate of survival 5568, Correlation matrix 17701, K NEAREST NEIGHBORS 8454, Excellent we are in the good path but 1522, lets plot some of them 35661, Correlation Matrix 8592, Standardising numerical features 2761, Just ensuring the distribution of data before and after filling the missing values remain s the same It s always good to have distrivution same before and after imputing the missing values 12626, Transfer the features into numerial values 4833, MSZoning since RL is the most common values we are going to use mode to impute it 40177, Import libraries 38573, Choosing a model 29588, We ll be normalizing our images by default from now on so we ll write a function that does it for us which we can use whenever we need to renormalize an image 7619, Lasso 33090, Defining usefull functions score cross validation 20969, Fitting the ANN to the Training set 11611, Cabin since there is too many missing value I remove it from dataframe 25371, Reshape 10715, Running Machine leanring python Chunk in R 36657, First off dilation monitors hits or contrasts to the pixels and adds an extra layer of pixels to the inner and outer boundaries of the shapes 1401, Pclass vs Survived 20451, previous applications 3487, Construct the best model and fit it to the training set 27479, The labels were given as integers between 0 and 9 6514, Continuous Variables 26332, Data Preprocessing 42850, no dis for no disaster tweets 18762, The plot 3d function plots the 3D numpy array of CT Scans 13894, Data Preparation 19188, we need to encode the categorical columns so that we can feed them to our model for the prediction Labelencoder is used for all the columns at once 15570, I have no idea what cabin T is supposed to mean so I set this to unknown 9476, This function creates a model and scores it using Stratified Cross Validation 33808, Baseline 5239, Numeric Features to be Treated as Categories 29016, Distribution of Embarked feature by Survived 25825, now there should only be a handful of categories per feature 25747, to an accuracy that s much better than chance It doesn t always guess zero either for size it guesses mostly That s bad set out to remove the correlation between shape and class from the data 1063, We try other kind of regressors such as XGBRegressor and ExtraTreesRegressor 9624, Test DataSet 5860, TODO Intro for K NN model 31006, We then reshape the samples according to TensorFlow convention which we chosed previously using K 1713, Advanced Imputation Techniques 22535, Training and testing 37641, Handling time 13578, On the Boat Deck there were 6 rooms labeled as T U W X Y Z but only the T cabin is present in the dataset 19268, MLP on train and validation 20217, Split the data into training and validation sets 35831, Handling the Missing Values 8578, Splitting the Data and Training the Model 42282, Load test data 9350, Cross validation 19828, LeaveOneOut 4475, I use this equation SibSp Parch 1 ownself to calculate the total family size that the person travelled with on board 22158, The first pipeline 587, Submission 3483, make predictions and write to csv 172, At last Final step let s make our submission 19357, Categorical columns within the dataset 19516, Creating RDD with Partitions 16285, First 5 trees 16875, Sex Pclass VS Survival 24904, Confirmed COVID 19 Cases by country 29390, The last thing that we need to do is compile our CNN model This is where we specify the loss function we use categorical crossentropy the optimizer we use adam so that the model s learning rate is automatically optimized and for easy evaluation the metric we choose is accuracy 18697, plot the confusion matrix 41823, For the data augmentation i choosed to 38215, Submit 27317, Rescaling features to bring it down between and 32202, Preprocessing 35496, Train Test Split 26528, Light Gradient Boosting Binary Classifier 38628, Import Keras 14346, Machine learning logistic regression 35830, Rescaling Normalization Standardization 16704, Completing a categorical feature 22004, Label encoding 31508, We use the ANOVA test to determine the feature importance 27009, Fitting 5112, Predictions 38717, The discriminator also use the same format except the number of neurons be different 2925, Spot Check Ensemble Methods 20088, Trend in Time Series 40025, I have created a dataset that covers some simple image statistics for train and test set images 36082, Loss 37790, Creating Training Generator 17984, we check the survival rate among both genders within the three ticket classes 16510, Data Modeling 21414, Relationships betweeen the sets 12900, Before we start modeling make sure we have no missing values on our training and test set 41009, a great idea which definitely helps adding relatives to the groups we identified so far 11039, Encapsulate all our feature cleaning and engineering 8763, Survival by Age 17904, Videos 8957, Label Encoding categorical features 36994, 9 of ordered products are previously ordered by customers 25497, Use the next code cell to label encode the data in X train and X valid 15179, Correlation heatmap 1082, Defining cross val score function for both train and test sets separately 39182, Extraindo features a partir das coordenadas 34795, Ridge Regression 4472, Similar to Cabin we created 2 columns leaving out Embarked Q to prevent collinearity 28514, OverallQual 24572, Observations 38160, We can also print a leaderboard with the training time in milliseconds of each model and the time it takes each model to predict each row in milliseconds 35658, Numeric Features 6685, Correlation Heat Map 14822, Name Title 36218, There are a lot of missing values here 4909, See who makes the Best Couple 26305, We droped some columns that less than of correlation of Sale Prices 14593, Observations 35442, Validation data 38011, All info about a single state 11380, Averaging the base models 38890, Initialise Adam parameters 403, XGB 6005, Kita bisa melakukan sorting berdasarkan data typenya namun kita harus berhati hati tidak semua yang terlihat numeric adalah data numeric bisa saja itu adalah data numeric yang berbentuk categoric kita harus melakukan sorting manual lagi nantinya 6240, It appears as if solo travellers are at high risk 41262, now apply this to the data set 32731, HyperOpt 32601, Applied to Full Dataset 35663, Scatterplot 17426, And how many did survived 4972, For age we can create equally spaced bins but for fare it makes more sense to divide into parts with same amount of individuals 12059, Factorizing 9267, Numerical data help better decide which rows to drop 9330, If we use GarageArea we get 16176, We can convert the categorical titles to ordinal 11604, LightGBM 31893, we implement our LGB Naive Ba classifier 25686, System with SOCIAL DISTANCING 1572, we impute the null values of the Age column by filling in the mean value of the passenger s corresponding title and class 960, Interactive feature importances via Plotly scatterplots 41441, vz is a tfidf matrix where 31312, The data for Store 1 is available from 2013 01 01 to 2015 07 31 14391, NOTICE the values of last newly added feature Title 24910, Confirmed COVID 19 cases Per day in India 25856, Above comment code tooks so much time 5311, Recursive Feature Elimination 1104, Random Forests Model 12304, XGBoost 511, AdaBoost 16569, Ensembling 7560, Prediction Time 27237, Predict using the best model 37442, Same here 33454, Reconstruct Train and Test sets 2219, Right Skewed Distribution Summary 37563, 1157 feature columns 35436, Getting the Train and Test sets for model to operate on 14489, now both frames are filled up now we are left with cabin 42055, Co relation matrix 36018, Encoder 13999, center Preprocessed data center 2716, For choosing the most optimal hyper parameters we perform gird search 22225, Etiket kodlamas ve veri setimizin train test olarak ayr lmas Label Encoding and Separation of our data set as train test 6835, Label Encoding Ordinal Features 27346, item cnt day 4514, Final Imputation 10724, Converting categorical to numeric 7015, Rates the overall condition of the house 34869, Ouch only 24 of our vocabulary have embeddings making 21 of our data more or less useless lets have a look and start improving For this we can easily have a look at the top oov words 7248, Explaining Instance 4 2 3 4 40992, Apply function can be also apply ied to the Time Series 12287, Empirical rule 4731, The describe function in pandas is very handy in getting various summary statistics 19856, calculate the boundaries outside which sit the outliers assuming Age follows a Gaussian distribution 12473, Age 22871, Normalization 35459, Visualize the skin cancer at torso 1182, Factorization 1530, Age Feature 41197, check if there is any still any categorical column 24305, because we want to reduce over fitting i use Data Augmentation technique 16178, Converting a categorical feature 6509, Numerical Variables 42108, Evaluating Model 20097, Making new dataframe for feature engineering 40747, Second Model 23621, Lightbm model 1577, We also fill in the one missing value of Fare in our test set with the mean value of Fare from the training set 3735, Get a baseline 42402, Sarima helper function 10245, Go to Contents Menu 34037, Modeling using Ridgecv 9325, Put the rare categories together by using the documentation 8610, now we have our dataset ready we fit several regression models and predict the housing sale prices 14695, Name and Ticket can be removed from the dataset as these features do not provide additional information about a passenger s liklihood of survival 548, Review k fold cross validation 25172, Lets look how pre processing changed our question text 31781, Exploring missing values 25295, we can try to project it again into polar space 9103, There are 3 rows where this is the case so I am going to assume the data collector accidentally forgot to calculate the MasVnrArea 29447, visualizing the target Distribution 37914, Performance 13546, Gender Column 31896, here is the prediction plot for var 108 27540, Display time range for labels with mean line 9899, Ticket 31921, Model Architecture 9186, perform linear regression on all the data at once 36257, look at target feature first 6907, In this case I decided to use mapping each Embarked value to a numerical value instead of creating a dummies 41472, Ensure AgeFill does not contain any missing values 15536, Create training and dev sets 38309, Dataset balancing 25258, Train and Test dataset 27643, I be doing some simple preprocessing to start out to improve the model quality and speed up training 21650, Reshape a MultiIndex df unstack 40074, One Hot Encode Categorical 27114, start filling these null values 6582, Add relative column 35159, Experiment Dense layer size 13791, Hyperparameter Tuning 17343, Adaboost 35090, Accuracy on Training Set 31996, compare the ground truth and CV DataFrames 28066, we create a function that imputes the age column with the average age of the group of people having the same name title as theirs 697, If we apply this threshold to the probabilities for our testing dataset we find out however that none of them pass the threshold 21171, plotting training and validation loss 32970, Lets count the missing values in train and test 42016, Creating a new Dataframe with certain rows 13221, Train test split 39977, Describing Dataset 17040, SibSp and Parch are correlataed with family size we drop SipSp and Parch as discussed in the chapter 34454, CA 3 41326, We write the top level of the decision tree to a 15802, Selecting the top 3 classifiers for model prediction 43147, look at how many bounding boxes do we have for each image 6068, Sell and Miscellaneous 30651, OMG we have missing values 821, List of categorical features and their unique values 15697, Number of parents children aboard the Titanic vs Survived 24772, Final predictions 26869, CNN model definition 18379, RMSE 5327, Display heatmap of quantitative variables with a numerical variable as dense 42370, Make model XGBRegressor 9279, Submission 23067, Command Center enabled features 17948, Encode FamilySize 26655, Transform features in float 5334, Diplay dendogram with many variables 15025, Fill NA 32765, Split to train and test 5130, Create Submission File 34793, The variability between the actual values and the predicted values is higher 6450, Hyper Tunning 30673, Try to apply DistillBERT 15020, Explore the Features 4262, MsZoning 34389, At this stage I m still a bit puzzled 18899, We use Pandas a Python library to read the train 4434, Making Predictions 41772, Label Nodes and Show Connections 20536, Linear Regression with Ridge regularization 16977, We first scale our data since some features such as Age and Fare have higher values than the rest of the features 3768, Magic Weapon 2 Xgboost Classifier 20485, Credit sum limit AMT CREDIT SUM LIMIT 35386, KNN 23611, Submission 28570, BsmtFinSF1 21575, Split names into first and last name 2193, Encoding str to int 43369, Reshaping Array 4926, XGBoost 32177, All of these variables have a bump of frequency that matches the rising of the probability of making a transfer 8762, Plot Ages 26106, It s an important and hard lesson for our classifier 38088, We can get a better sense for one of these examples by visualising the image and looking at the label 33819, This submission should score about 0 19519, Setting Name of RDD 1550, First let s take a look at the summary of all the data 40619, And a GBM 40668, Ngram Analysis 32915, On Kaggle the kernel can run for at least an hour I can t train the model here 12193, More often than not you need to create transformers that do nothing while fitting the data and do a lot of things when they transform it 27385, Thresh 60 915, Normality And Transformation of Distributions 515, Creating Submission 13863, Ticket 41488, Take the decision trees and run it on the test data 19655, we define each entity or table of data 39847, AutoCorrelation Plot 24990, As a sanity check checking that the number of features in train X numerical match the total number of numerical features 6311, Adaboost 13056, Support Vector Machines 1861, KNeighbors 5086, I have looked at the features and found a possible error in the test data The house was sold BEFORE it was built 34414, Number of characters in tweets 9976, Finaly prepare submit 13398, Predict accuracy with different algorithms 5330, Display heatmap of quantitative variables with a numerical variable as dense with custom gradient Similiar to heatmap 16598, Describing all the Categorical Features 29031, Convert categoricals to numerics 28949, We finalize the model by training it on the entire training dataset now 834, Correlation Matrix 2 All features with strong correlation to SalePrice 39230, Demonstration how it works 36656, And finally bilateral filtering utilizes Gaussian filtering twice in order to preserve edge detail while also effectively removing noise 12691, Family information 36783, Accuracy 6144, Living Area bins 11108, Scale values 32135, How to get the positions of top n values from a numpy array 8559, Selecting Rows Based on Conditionals 37464, Prediction and Submition 8759, Survival Count 7364, Final prediction blending 28853, Date List 38663, Bivariate Analysis 37374, Random Forest 41656, Distribution of data for categorical features 36277, corr is the highest correlation in absolute numbers for Fare so we ll use Pclass again to impute the missing values 40333, Prepare Universal sentence encoder embeddings 36261, It s Time to look at the Age column 26675, Days employed 37874, Standardization 25678, Conclusion 8045, Embarked 11859, Fit the models 35579, Sale Prices 42159, 99 of Pool Quality Data is missing 5616, Etc 3D 465, Making dummy features for each categorical column 31290, Problem solved 16349, Create new features Title FamilySize IsAlone AgePclass NameLength HasCabin 7258, Data Visualization 40631, Exploratory Data Analysis 18721, We shall use slice as our sequence of learning rates 32663, Categorical values must be quantified into numerical equivalents to be properly processed by machine learning algorithms 7096, As some algorithms such as KNN and SVM are sensitive to the scaling of the data here we also apply standard scaling to the data 35943, LogisticRegression 21095, Reciever Operating Charactaristics 12258, Inference 8503, Observations 10198, Lets convert them into numerical form 24725, Feature Engineering 20255, Prepare the submission file 34437, Download data 22040, the next important variables from EDA are floor and max floor 33291, Scaler 11549, drop columns where the percentage of missingness is greater than 90 35307, Set optimizer and loss function 12743, visualize those missing values in a heatmap 4401, Splitting the Dataset 21205, Zero initialization 21020, The dataset is from the competition where our job is to create a ML model to predict whether the test set tweets belong to a disaster or not in the form of 1 or 0 10822, double check it 23234, Write a useful function 27022, Submission 9684, Loading data 19522, Repartitions and Coalesce 26751, image in 3 dimensions 19073, Visulaly analyzing 15800, Since categorical features have been created from the features present in the dataset taking only the categorical for training the models 21419, Train the model predict etc 3777, Training 32216, Add total shop revenue per month to matix df 38283, Imputing data in Garage related columns 15581, Submission says Your submission scored which is not an improvement of your best score Keep trying 15796, Most of the Passengers aboard were alone 13422, Data Transformations 13147, Title as Survival Function 29927, Hyperparameters versus Iteration 1789, Missing Train values 10106, fill NULL values 11383, we re ready to load the data 7024, If Central air conditioning is present 24386, look at which values have what percentage of missing data 34347, Create Final Model and Predict 11405, Building Your Model 23712, Extensive Feature Selection using Wrapper Methods 32156, How to fill in missing dates in an irregular series of numpy dates 1635, Obtaining standardized dataset 5092, Load data 29328, Random forrest 27140, MSSubClass Identifies the type of dwelling parts for sale by the owner involved in the sale 29406, let s import our datasets both train and test 12618, combine SibSp and Parch features to create new one FamilyMem 42661, as in the univariate case we analyze correlations between missing values in different features 21590, Count the number of words in a pandas series 9753, AgeRange 3761, KNN Regression 1129, Exploration of Passenger Class 297, Correlations 31923, One of the hyperparameters to tune is the learning rate and the decay 32196, Clean up some shop names and add city and category to shops df 28722, The Item Solds by Shop Names 34518, Installments Payments 11896, Data Engineering transform all non numerical data into numerical processable ones and fill missing values 38296, Predict the test data 6843, Submission 37877, Alpha 16126, Create Submission File to Kaggle 24234, Time to predict classification using the model on the test set 16778, Grid Search CV 31216, How are numeric features related to SalePrice 38674, Age 14084, We fill the missing Age data with the mean age of passenger of each class 27659, Model training 13550, fill na s in Embarked 13584, Modelling Pipeline of models to find the algo that best fit our problem 32556, Target 39172, And to try the created CNN use the following code 11621, Preprocessing 34399, Split the data and labels 1727, Plot variables against Survived 290, The letter prefix might provide useful information for the model 12884, I think this warrents some more investigation 11406, Checking Underfitting and Overfitting 17560, do all the transformation needed 42918, Categories 16062, Correlation Correlation Matrix 31615, A good way to measure the performance of a classifier is to look at the confusion matrix 3543, the mean age is 29 7123, SibSp vs survived 3774, Embarked 19450, We load the dataset and verify the dimensions of the training and testing sets 25826, Truncated SVD on categorical data 6522, Replace the missing values in test set with median since there are outliers 970, Read our files 15377, See how the mean age differs across Pclass 18680, Creating Submission 34101, Statewise cases 40954, Assignment of Variable 36133, Predictions 23980, Predictions 34756, Correcting spellings 25501, For large datasets with many rows one hot encoding can greatly expand the size of the dataset 35900, fit our model to the training set 41609, we create a dictionary for the second part of our analysis called department time data in a similar manner that we did before 27045, Distribution of Diagnosis 38080, In the train data and test data there are no missing values 37395, please 26647, we have 1 27512, Rescale features 23682, Train the VAE 7971, Completing a numerical continuous feature 18273, BUILDING A RANDOM MODEL AND FINDING THE WORST CASE LOG LOSS 31247, Family Size 40020, dicom images and the image names can be found in the train and test info as well 10792, Excellent 5305, Bringing features onto the same scale 22055, Distribution of sentiments in train and test data 43264, Instancia um objeto chamado dt a partir de uma classe DecisionTreeRegressor 29850, prepare the testset 845, SGDRegressor 18627, Creating new Features 12409, Utilities 14352, Analyzing Features 17356, The perceptron is an algorithm for supervised learning of binary classifiers 8854, Interpreting the Model With Shapely Values 9293, Regression on survival on Age span 11482, GarageYrBlt GarageType GarageCars GarageArea GarageQual GarageCond GarageFinish 4968, We can pick categories for names with at least 2 individuals 24639, Similar to Simple Linear Regression there is an equation for multiple independent variables to predict a target variable The equation is as follows 12424, We be using the same training data for Backward Feature Selection 33807, Visualize New Variables 3010, strong Transforming values strong font div 23049, ok now lets Split training and valdiation set 15228, Drop Correlated Features 3491, Make predictions on the test set and output csv 23514, There are 7 elements in the class 1733, Pclass vs Embarked 2152, Functions 8132, Resizing and viewing 20676, The three most common loss functions are 13320, Analyze by visualizing data div 34293, Train Convnet 37831, Test against the Test Data 43380, Attack class 21059, Show Attention 2083, Upon adding the feature Surname the CV score again increases to 0 784, SVR 11386, More people died than survived in the training set 25031, Network Evaluation 3283, ElasticNet 11982, Will we use One hot encoder to encode the rest of categorical features 4822, Feature engineering 41332, We again save the top level of the t SNE decision tree to a 31943, Looks good 41940, Training the Model 33643, Below I impute missing records 29740, Training LightGBM model 9096, I am also interested how the number of stories affect Sales Price 42554, Precision Recall Curve 25589, XGBoost 26981, Normally we would prefer to map pixel values to 0 1 to avoid slowing down the learning process 27258, have a look at the feature importances across all of the emsemble models 14254, Fare Features 18024, Only families from Pclass 3 died together 15601, Survival by Age Number of parch and Gender 34640, Splitting Data 14667, PCA 16728, by survival 18928, Relationship between numerical values with a categorical field 13587, Best params 31044, Pick Sentence 23521, Equivalence classes with mislabelling 27508, First By Normal fit method method 22895, Train and test have the same set of keywords 2578, BernoulliNB 6193, Submission 1 for Logisitc Regression without Hyperparameter Tuning 32690, We build now a CNN try with a simple one consisnting in 5 Conv layer one dense layer and one ouput layer 26513, After training is done it s good to check accuracy on data that wasn t used in training 3278, Feature Engineering and Transformation 32564, Diff anatom site general challenge 19883, Introduction 27284, Char TFIDF 33668, Time font 24851, Splitting my dataset with 90 for training and 10 for validation 42127, Data generator 18524, In order to make the optimizer converge faster and closest to the global minimum of the loss function i used an annealing method of the learning rate 22436, Scatter plot with linear regression line of best fit 20552, Data preprocessing 18921, Testing Different Models 8258, Checking each feature s type to know which value to impute later 18441, Compare Model font div 32566, Diff Age approx 9049, KitchenQual 26942, Are the distributions making sense 13380, Imputation of missing values in Embarked 35309, Make predictions 36665, Exploratory Data Analysis EDA 10743, Check the relationship between SalePrice and predictor variables 316, Exploring 13558, Fare mean by Embarked 24995, Selecting best categorical variables 42270, bedrooms from 1 2 interest level increases since number of low decreases while number of medium and high increase 1757, In the following chunk of code I use a code from a very experienced fellow Kaggler But on top of that since I mentioned that coding is an integral part of data science and I am not experienced with coding I make an attempt to break the code into easy chunks to understand experienced coders can skip the explanation 26027, Inorder to help my model better understand what data its going to face I am converting all the encoded variables into dummy variables aka one hot encoding 26561, Put all together 37534, Importing Keras Libraries 16264, Cabin 21666, Load Pretrained Models 9977, There are 1460 instances of training data and 1460 of test data 19012, Experiment 2 27848, Top 20 3 gram words in sincere and insincere questions 12073, Data Cleaning 1790, Missing Train values 32965, Baseline Submission 22674, Getting Things Ready 15393, See which columns have missing values in test data 38785, Multiply by frequency distribution 29890, fit to the whole training data 13865, Cabin 36205, Evaluating the BERT Variations 26737, Plotting sales over the months across the 3 categories 33871, Ridge 2947, Convert Categorical features into Dummy Variables 13573, Ticket feature 6315, Gradient Boosting Classifier 2203, now the gender column getting rid of the categorical strings and making new dummy columns 495, Majority of people who survived embarked from S 28316, Exmaine the bureau balance DataSet 38110, Model Training 4748, first we trying to find out date time variable and then trying to plotting them and analyzing them 18251, Feature with only 1 or 2 values 1571, This first function creates two separate columns a numeric column indicating the length of a passenger s Name field and a categorical column that extracts the passenger s title 14002, The good correlation between Embarked and Survived makes me wonder 35135, Does competition distance matter 30759, Build the benchmark for feature importance 6400, Pairplot 28765, Splitting data using sklearn 3719, Checking for missing values if any 27413, nn model The general methodology to build a Neural Network is to 27039, Gender vs Target 42811, Adversarial validation unfinished 28530, HalfBath 33704, I select top most 100 sold items 6530, Stack Models 5971, Classifier Comparision 15374, Submission File 42848, Rest of the World 30755, Fixing sub samples 4043, First let s take a fast looking at the table we have 19153, Select the right class weight 40298, Questions Exploration 39712, Compare the similarity between cat vs 2499, Parch 23294, MasVnrArea MasVnrType have the same MissingValueRatio 8 so probably these houses don t have a masonry veneer We ll fill the missing values with None 24355, Evaluate the model 31021, Find and convert emoji to text 10725, Lets drop and combine some features now 24755, Label encoding 10871, Build a Name dataframe 6436, Statictics Helping hands for a Data Scientist 7498, Create output 20692, Running the example loads the MNIST dataset then summarizes the default train and test datasets 10851, Import models 224, Decision Tree 10817, Data looks accurately 32838, We are done with most of the feature engineering part 16600, How many missing values are there in our dataset 35904, Load Required Packages 23232, far you ve learned how to build pipelines with scikit learn For instance the pipeline below use SimpleImputer learn org stable modules generated sklearn impute SimpleImputer html to replace missing values in the data before using RandomForestRegressor learn org stable modules generated sklearn ensemble RandomForestRegressor html to train a random forest model to make predictions We set the number of trees in the random forest model with the n estimators parameter and setting random state ensures reproducibility 8538, Feature to Feature Correlation 15721, Logistic regression 38617, Just to be sure let s check if there are any duplicated descriptions 20804, We create extra features in order to categorize data with and without a feature 16633, Class distribution 2200, we need to impute age column basically fill in the blanks I m filling the blanks based on their Priority Class means 6212, Gradient Boosting Classifier 14385, I am grouping age with following categories 16472, Ensemble Modelling 15256, Fill Null Values for Embarked Feature in Train Dataset 14900, Pclass Fare vs Survived 16112, Feature Selection 11157, MSZoning The general zoning classification RL is by far the most common value we can fill in missing values with RL 18381, And now we start building a new smaller dataset from twitter from scratch 40418, Number of Photos 40645, 2D Visualization using TSNE 14782, Possible derived features 42128, Model 6749, Drop Features have more missing values 868, Age continuous numerical to 8 bins 20486, Comparison of interval values with TARGET 1 and TARGET 0 5159, Linear Regression 42865, We encode our features 10894, since we have imputed the missing age values again divide the age variable into three groups child young and old 37331, Select the parameters for the second dropout 34490, Lets get the answer for the test data 37409, remove the features that have zero importance 1179, Incorrect values 43328, Detect and Instantiate TPU Distribution Strategy 8656, far we have identified three columns that may be useful for predicting survival 7116, Train the data 28786, Most Common words in our Target Selected Text 29557, For this task we try to use LSTM network 33854, Analysis of ctc min cwc min csc min token sort ratio using pair plot 12011, SVR with linear kernel 37692, we want to train our neuron This means we want to make predictions closer to the true values 27593, We can also include the oov token parameter in the Keras Tokenizer object so that when the tokenizer finds words that our out of vocabulary in the test set instead of skipping them it includes them as a token of our choosing we just need to ensure our token does not resemble any other words in our vocabulary However since we ultimately embed our words as GloVe vectors this step is useless But if you were using your own embeddings it could prove useful so I include it below 6889, Another way to calculate these using groupby 32206, Feature Engineering 22866, Input Image 10846, Check now is there ant missing value remain 10844, separate the catgorical freatues and numerical features 8655, Create the cut points and label names lists to split the Age column into six categories 5610, Minimum Size 9870, Pclass Survived 12713, Everything that I want to carry forward to the machine learning stage looks ready 10997, Age Cabin and Embarked are the features that have missing values in the Train dataset 35671, Categorical features 35330, Encoding the target as usual 15293, Building and Training the Model 10334, Removing Outliers 27381, RMSE 1 27481, Another important method to improve generalization is augmentation 21153, check what random forest can do in this case 1364, Librarys 1653, Another nice categorical feature here with 2 categories But wait in tutorial for beginners part 1 we found that Sex was a very strong explanatory variable after all 141, Support Vector Machines SVM 33747, Stack more CNN layers 15036, Is there any discount for the old man Possibly not 671, LightGBM 19972, A simple model with just a Softmax classifier 41076, Remove empty comments 8394, Creating a normalized entity to cross throught our main interest table 28082, This is the in sample accuracy which is generally higher than the out sample accuracy 29405, SAVE TEST AND TRAIN AS CLEAN 22157, Splitting the dataset to train valid sets 7119, Loading Data 37637, we make predictions on the test data and create a CSV submission file 38750, Model Building Evaluation 32037, we have the best parameters and the best estimator which gives the highest mean cross validated score But we don t have the threshold for our classifier instead of using best estimator we make a train validation split on train X train Y we train and validate a new classifier on this new split with the best parameters found in grid search The reason to do this is to compute the threshold for our classifier 14503, Cabin 3324, Explanation 12513, get to work using LassoCV 37898, Residual Plot 15753, Removing Pclass 3 and Embarked S 8424, Some Observations Respect Data Quality 14597, Observations 20203, Check the missing percentage of missing data and drop those columns 9094, I want to have a binary column for sharing a floor 38689, Probability of melanoma with respect to Sex 24345, Reshape 12281, See the importances of features 1618, Feature Importance 28563, SalePrice is clearly affected by BsmtQual with the better the quality being meaning the higher the price 13369, females have higher probability of survival than males 35578, It appears that walmarts are closed on Chirstmas day 13099, Survival among Passenger Class Sex and Embarked 21781, We do some simple math to find out what block should we train on 18101, Batch Normalization becomes unstable with small batch sizes and that is why we use layers instead 41932, Discriminator Network 35083, Applying PCA and Transforming x train scaler and x test scaler to x train pca and x test pca respectively 25003, Preprocessing test images 19407, Lets predict for testing dataset 20778, Fitting a T SNE model to the dense embeddings and overlaying that with the target visuals we get 7836, Sqlite and SQlAlchemy 2132, Once again we use these information to iterate a few more times on the various configurations 8520, Ridge Regression 36829, we use the classifier to label the evaluation set we created earlier 36831, Retraining 317, Feature Engineering 6121, We need more different zones Milord 11529, Gradient Boosting Regression 34644, Random Forest Classifier 2924, Support Vector Machines 31348, Submission To CSV 28478, New features based on the address 31553, Data Normalization 28510, The numberical columns have following characteristics 16829, Checking VIFs 8943, Fixing Utilities 8079, Creating Dummies 18240, Hyper parameters and network initialization 12092, Apply the model on the test data and create submission 32134, How to replace all values greater than a given value to a given cutoff 8329, The white elements have nan there 3262, strong strong strong Creating a table of columns with maximum missing values p strong 42789, Word Frequency plot of sincere insincere questions 35765, This model needs improvement run cross validate on it 21469, We cannot do our transformations blinded 31043, Search 18420, save the oof prediction for further use 3269, Get percentage of these missing values in features 33705, Transposing all 100 items into columns 7254, Model parameters 27539, Display time range for labels 2051, GaussianNB Model 41750, Hyper parameter tuning of Keras models using Sklearn 2129, And the following in fold and out of fold predictions 20213, Handle null values or missing values 32776, Ensemble 31555, Linear Regression 23411, load and preprocess the data to test our network s possibilities 7809, Blend Models 34339, Categorical Features 6994, Month Sold 25223, LotFrontage Linear feet of street connected to property 3956, Create PorchSF Feature 24317, first imput the missing values of LotFrontage based on the median of LotArea and Neighborhood 42261, some entries haven t the date they joined the company 41408, AMT CREDIT 14456, Train Test Split 22476, Stacked area chart 19002, Prepare learning rate shrinkage 28526, LotFrontage 19596, Trend Analysis 10872, Build df train and df test data frames 38507, clean text function applies a first round of text cleaning techniques 40071, Looking at the heat plot for cross correlation 17406, Parameters 935, Prepare Data for Consumption 18312, not much correlation heare 41071, Data preparation 2545, For this part we expolore the architecture with just one Hidden Layer with several units 30671, LinearSVM 4277, GarageArea GarageCars 32819, Data Engineenring 43149, Preprocessing Data for Input to RetinaNet 3552, There is much more male than female on the Titanic but what about the survivals didn t guess that already the survival non survival ratio is much greater for the female than the male isn t that obvious like we say les femmes d abord 25373, Split training and valdiation set 42853, WordCloud Most frequent keywords in the tweets 3379, From here you re pretty much done In the last piece of code below we ll simply generate a csv file in the format that we can use to submit to the competition namely with two columns only for ID and the predicted SalePrice 36757, Visualizing Number of Images for each Digit 16887, Big Family 40878, Learning Curves 4049, Cabin and Ticket Analysis 2087, since we want to evaluate our model in order to be able to say how good or bad it can be in certain situations we need to create our test set 31543, replacing with NA due to lack of subject 17740, As a side note it s interesting that deck E contained 1st 2nd and 3rd class cabins 5857, The last step is to analyse the predicted variable in this case the Sale Price 31792, Construct model Some hacks to get gradients 13428, Run iterations for all the trained baseline models 5309, Feature selector that removes all low variance features 29040, Class label distribution 29542, Checking the metrics now 3863, Bonus Significant parts of these helper libraries are currently under development to be included in sklearn library 25898, Printing keywords 5999, Hasil 19814, BaseN Encoder 9794, Since this is a regression problem 31783, Exploring correlation of features between the train and test sets 2700, Based on the previous correlation heatmap LotFrontage is highly correlated with LotArea and LotFrontage 10243, Go to Contents Menu 24794, Modules 27333, Data description 6746, Count Plot 2310, Sklearn How to scale fields 4048, We can also dummify the sex column convert the categorical data to zeros and ones 21604, Reading JSON from the web into a df 6294, Naive Bayes 12782, Neural Network s Layer s 23482, MIN DF and MAX DF parameter 11143, Compare the r squared values for different functions 13310, Gaussian Naive Bayes 16501, Perceptron 28855, Plot 9477, This function tunes the hyperparameters of a model and scores it using Stratified Cross Validation 543, Bining for Age and Fare convert Title to numerical 9838, Gaussian Naive Ba 1706, Imputations Techniques for non Time Series Problems 7020, Refers to walkout or garden level walls 22164, Finished with your model and push it to prod 19179, Training Summary 20922, evaluation 3537, Missing Value Analysis 11057, Age versus survival 21422, LotArea 724, the mode is SBrkr 26515, Appendix 3864, Numerical Features pipeline 4977, IsAlone and LargeFamily 32355, Fitting 14753, Additional Variables 42258, Calculating the Hash Shape Mode Length and Ratio of each image 10451, Kernel Ridge Regression 12273, Submission 41541, It does look better slightly anyway 25764, PassengerID 6760, Checking Skewness for feature 3SsnPorch 7763, Linear Regression 6546, top 40 correlated columns after data preprocessing 31918, Augmenting Data 18363, Visualse the categorical features 6767, Logistic Regression 27166, For this section we create a separate dataset 6232, Split training validation test data 4340, Third class 31763, Create the product list for each order with filtering in pandas 17864, We prepare as well the second level classifier 880, train test split 36398, Different options 29612, Dataset explanation 1607, Frequency Encoding 30917, Find best threshold on Trainin data 22394, Data Cleaning 4636, EDA for Categorical Variables 28603, OverallQual 29824, PreTrained Glove 2474, Mapping SEX and cleaning data dropping garbage 26821, check now the distribution of the mean value per row in the train dataset grouped by value of target pre 37302, Bi grams 26033, we have defined our transforms we can then load the train and test data with the relevant transforms defined 461, Same for the categorical data 4357, We can also generate one new feature using YearRemodAdd feature 22684, Train and predict 3652, Looks good 8671, CatBoost model 8347, Just a quick look on variables we are dealing with 15845, Embarked 27079, start by experimenting with LSA This is effectively just a truncated singular value decomposition of a very high rank and sparse document term matrix with only the r n topics largest singular values preserved 33448, RandomForestClassifier 41589, CORRECTLY CLASSIFIED FLOWER IMAGES 25363, Simple Model Logistic Regression 41046, we select at random the clusters that form the validation set 2192, Imputting missing values 10458, Pearson correlation coefficient 32280, Display distribution of a continuous variable 26047, Training the Model 16058, Here is only age no relation with family passenger is calculated alone with his her age 20384, As we know that there some words that repeated so little in our tweets so we must remove these words from our Bag of words to decrease dimensionality as possible 42471, With SelectFromModel we can specify which prefit classifier to use and what the threshold is for the feature importances 40062, Visualization 43 categorical 38 numerical columns 32205, Add shop items and categories data onto matrix df 39284, Export aggregated dataset 13705, Here again it appears as if no cabin data is correlated with lower Fare value and decks B and C are correlated with higher Fare values 15, Hyper parameter Tuning 1290, A confusion matrix is a table that is often used to describe the performance of a classification model 21772, Last columns with missing values 30386, Preparing submission 42958, Ticket Feature 1947, Finding the correlation between the different features 9521, Support Vector Machine SVM 34866, lets populate the vocabulary and display the first 5 elements and their count 37682, Test if saved model works w sample inference 20197, Check variance 4407, Cross Validation CV 26030, MODELING AND EVALUATION 12363, Utilities 39501, Target variable 28859, Normlize Scale Data 30084, K Nearest Neighbour KNN Algorithm 13196, Introducing family aboard no child couple and features related to family size 2671, here we identify which are highly correlated with other variables including these features not add any additional information to the machine learning modelling Ideally we should exclude all these features The threshold can be changed from to any number based on the business scenario Below we exclude all the correlated features 16526, LogisticRegression 38550, have a glance at new features 27248, Feature selection 32815, Well for training score we manage to arravie at 0 36344, Implement Backward Propagation 11686, Bin numerical data you want to bin the numerical data because you have a range of ages and fares However there might be fluctuations in those numbers that don t reflect patterns in the data which might be noise That s why you ll put people that are within a certain range of age or fare in the same bin You can do this by using the pandas function qcut to bin your numerical data 39692, Text Preprocessing 28369, text 34638, We use the test data 13311, Perceptron 38276, check our predictions 27083, All that remains is to plot the clustered questions included are the top three words in each cluster which are placed 40175, Ensemble Performance 11192, Optimize 5394, Most of the missing values are in Age and Cabin columns 2984, XGBoost 41919, Since we ve got a pretty good model at this point we might want to save it so we can load it again later without training it from scratch 39216, New have a logloss calculation such that we re not totally in blindness 17714, We are going to use our single models in the first layer and LogisticRegressor as metalearner 34167, Time series analysis 42365, Building the Feature Engineering Machine 30778, Score of trained model 11715, Logistic Regression 12172, Split the training dataset to understand in and out sample performance 7303, Observation 195, Gradient Boosting Regression 8926, Fence 28823, An easy getting started with time series analysis is 1782, We come up with a function called Acc score that gives the mean of all cross validated scores through the CV K Fold 40415, we have data from April to June 2016 in our train set 40200, Confusion Matrix 23037, Department Analysis 8676, The Features and the Target variable 26911, Build the final model with the best parameters 12342, GarageFinish Interior finish of the garage 2651, Interesting 10013, Box Cox Transformation of highly skewed features 7345, the categorical columns that are important to predict the SalePrice are ExterQual BsmtQual and KitchenQual 21258, Fine Tuning and Using SVD Collabrative Filtering algorithm using Scikit Suprise 35813, Date features 1200, Lasso Regression L1 penalty 18164, Checking the character in question 30865, Label encoding Target variable 41123, Ventura County Average Absolute Log error 32562, Diff Target 20326, Section 5 Concluding Remarks 21815, Latitude Only 42526, As there are many countries for which the province state column is nan so i thought to put the country name if it is nan 41281, Perform automated EDA using visualizeR 27279, If DAYS EMPLOYED is a large positive number means the client is unemployed Maybe extraxt those with dummy variable It would applying for a loan unemployed lowers your approval from 8 2755, Understanding the distribution of Missing Data 34229, we have our actual data array we need to make some adjustments 17421, LogisticRegression 26385, The hello world dataset MNIST released in 1999 contains images of handwritten digits 4685, Here also 41480, Drop the columns we won t use 3602, Optionally look at column descriptions 4290, TotalSquareFootage 3567, The higher the quality the better the selling price 24134, we can calulate F1 score for all models 16144, how titanic sank 36676, Tuning the vectorizer 22433, When you create a gridspec like 3 x 3 to post a plot into this grid you must use index slicing span 9036, If the skewness is between and the data are fairly symmetrical 38254, Simple Mean Model 15141, Check the Survival Rate 30956, We have to pass in a set of hyperparameters to the cross validation so we use the default hyperparameters in LightGBM 30840, Crime Distribution over the years 35551, Blending 8502, Focusing on Sales Price 23024, I don t understand why mean NaN when using 1188, Creating features 9503, Import all required or necessary libraries 8732, Scale Sales Data 26428, The inspiration for this feature came from 9098, Basically price increases if the house is 2 stories or more 31417, Create the Estimator create the estimator 39007, Build the model and run the session 33877, Voting Regressor 18189, clean stop words and leave only the word stems 28217, Correlation matrix 8323, Maximum people are with no siblings travelling 35935, A B C T are all 1st class 2989, Model Performance Plot 23296, GarageCond GarageQual GarageFinish GarageType have the same MissingValueRatio 81 so probably these houses don t have a garage We ll fill the missing values with None 13954, Load data 37214, Tokenize Training Text 22346, ML Modeling 31809, RPN layer 39002, Create placeholders to pass training data when you run the tensorflow session 2905, First Data Exploration 32387, First Step Creating Meta Submission 14526, Fare 43094, We run the model 39972, Model Data 31055, Subword 6088, Examine correlations between SalePrice and target 13121, Using subplots 7069, Here I used the other models not yet trained to be combined with the Stacking Regressor and make a powerful one using the Voting Regressor 10934, Let find out degree centraility of all nodes of directed graph g 38758, k Nearest Neighbors kNN 28006, Data Augmentation 19274, We need to convert our data from the dataframe into a matrix of inputs and target outputs 28942, Before preparing the model we can drop the below columns as they are not useful for the model 41416, Label encoder 7774, Pasting 5324, Display density of values with bubbles over latitude and longitude 21169, optimizer 13989, Read test data 43004, Data cleanning drop duplicate rows 24571, Observations 22183, ELECTRA span 12718, Always the most interesting in my opinion anyway The imputed age feature went down a treat with XGBoost followed closely by the new Family Survival feature 28015, Any of these rows reveals the vector of each document 34441, Submission 1816, As we discussed before there is a linear relationship between SalePrice and GrLivArea 4630, Find the count of missing values 41184, first analyze missing value counts in numerical columns in training data 7204, let s compute the RMSE on train dataset to evaluate or model error 28317, identifying Catergical and numerical variables 18852, Test and Submit 16689, find the relationship of survival between different Source stations 7553, ADA BOOSTING 15096, Classifiers 35947, RandomForest 32714, GRUs 6709, Find Features with one value 5400, The embarked is probably decided by or correlated to Fare and Pclass 34704, Item active months 26352, Correlations increased after the outliers were removed 10089, Model Selection 7952, Tuning on weight constraints and dropout 11469, k Nearest Neighbors 59, Pytorch 28290, Some NN train settings 22410, There s some rows missing a city that I ll relabel 32528, Predictions 11228, let s take a look at the people s survival status if he she had friends or Sibsp based on the same ticket number 39435, The final submission 19701, Basic Data Analysis 28960, Temporal Variables Eg Datetime Variables 27907, Drop All Columns where NaN occurs 27993, RandomForestClassifier 11409, Using Categorical Data with One Hot Encoding 35893, Plot the evaluation metrics over epochs 16657, Fare 37524, Pclass Survived Age 40830, A lot of inferences that we have already covered could be verified using the following heatmap 31062, Positive look behind succeed if passed non consuming expression does not match against the forthcoming input 19540, Specify and Fit Model 40388, We explan repeat and batch together 9189, We use the average age of the corresponding class to fill the missing passenger ages 34791, As the target variable is a highly skewed data we try to transform this data using either log square root or box cox transformation 6430, Clearly performance is better with Random Forest lets make final submission with RF model 22657, The Model 38000, let s make some plots 20050, The store names are the same but they don t open and close at the same time let s check if store id is in the test data 28013, MAKE YOUR HANDS DIRTY 17768, Sex Age SibSp Parch 38071, Vocabulary and text covering embeddings 38042, Conditional Probability 6541, now we are saperating categorical columns and numerical columns for filling missing values 1202, Xgboost 12493, predicting prices with ElasticNet Regressor 35493, Loading the Data Set 18198, Distribution of counts by product 20584, Encoding Categorical Data 16872, Survival Correlations 12123, Fence data description says NA means no fence 13396, Feature Scaling 13481, Resources 8822, Feature Engineering Age AgeGroup 27004, Splitting 31226, Features with positive values and maximum value between 10 20 40304, Q1 Q2 neighbor intersection count 30659, People never lie about the wreckage derailment and debris 42136, Predictions class distribution 13665, Cross Validation K fold 7347, The target variable is right skewed 21456, Modelling 25236, Since Lasso is wininng in predictions here are the coefficients 34751, Building model using Glove Embeddings 2777, CatBoost Regressor 32835, We have done the pre training of the model now we build our model using BERT 39306, XGBRegressor validation 22290, Scaling 12259, Interpretability 17557, Similarly for Age column 39012, VGG19 is a similar model architecure as VGG16 with three additional convolutional layers it consists of a total of 16 Convolution layers and 3 dense layers also stride 1 28876, Split Data 15838, Cabin 2005, Only 0 29161, MasVnrArea Fill with 0 32478, Build Demographic Based and Memory Based Similarity Matrices 34683, Including all possible combinations of unique shops items for each month in the train set 6584, The challenge is we have to use machine learning to create a model that predicts which passengers survived the Titanic shipwreck 42621, Clustering the data 4003, One solution to the latter drawback is to gradually decrease the learning rate 14842, However when it came down to modeling these fare categories did not help at all as they underfit quite substantially 13248, PassengerId is distinct so delete 23485, Creating a Baseline Model using Countvectorizer 41064, One hot encoding of label 41356, Generally houses with high level basements have higher prices than others 29086, Embed building id 25520, CatdogNet 16 2285, Precision and Recall 12168, Class Weight 15382, Notice what happens when you replace the missing fare by the mean fare instead of mean fareperperson 23417, Training the full model for 100 epochs leads to 99 34036, Transformation of target variable 17895, Lets do some predictions now 17792, verify the average survival ratio for the passengers with the aggregated titles and sex 20510, import the data 19597, shop revenue trend 16380, Changing the DataType of Age and Fare to int 5007, Bivariate Analysis 37136, Binary Classification 32754, Monthly Cash Data 25394, Evaluate the model Model with data augmentation 24339, But if we average only two best models we gain better cross validation score 4913, Concat the Training and the Test Data to a Complete Dataframe 4866, Preprocessing 40295, I am just setting my network from what is called Convolution Neural Network 36229, To read more about CNNs you can refer to this 1147, We are going to do same thing to the test data 27655, Data Visualization 40336, About leak 7459, the Age column needs to be treated slightly differently as this is a continuous numerical column 5691, Model Building 3927, Support Vector Machine 1797, SalePrice vs TotalBsmtSF 32847, take a look at the raw data 38131, Range of fare was as follows 2046, SVC Model 5587, Re Check for missing data 20116, Because of using 12 as max lag feature we need to drop first 12 months 16014, Age 1183, Skew transformation features 42449, Example to extract all nominal variables that are not dropped 40793, Check null values 11867, Checking skewness of all the numeric features and logarithm it if more than 12692, Group information 10905, Grid Search for Bagging Classifier 3023, We check the data after normalisation is there any remaining skewed values 41043, we extract layer 4 VGG features from the images 31261, Import libraries 40189, Using my notebook 9355, Converting a categorical feature 2698, Since MasVnrArea only have 23 missing values we can replace them with the mean of the column 11962, I have added 0 to wherever required while creating extra features and adding to the test set This is due to the fact that while doing OneHotEncoding there were some extra values in the train set but not in the test set Thus create those features insert 0 in values and concat it to the test set 30429, Evaluation 21418, split the variables one more time 41179, Submission 37983, Model2 Simple CNN but with tunned hyperparameters 17699, DECISION TREE 4522, Going with Random Forest Regressor 31073, Delaing with MasVnrArea 21632, Pandas display options 29119, Time to make some predictions 37722, Constant and Duplicate features handling 7693, Ridge regression 40280, making 5 fold divisions on the dataset csv 7843, let s try to get the title from name 3758, seperate the data as it was 33717, FEATURE CABIN 4944, however notice that this is just a demonstration and we did not in fact change our data all data we however deal with outliers later using StandardScaler RobustScaler 25824, PCA on categorical variables 28591, TotRmsAbvGrd 1599, After filling the missing values in Age Embarked Fare and Deck features there is no missing value left in both training and test set 39256, Distribution of target value 15284, Average Fare for each Pclass 40187, There are problems in processing 5662, Convert feature variable Sex into dummy variable 9691, Checking for Linearity 1758, Attention 30099, Dataset visualizations 929, Optimize Support Vector Regression 23263, Categorical Continuous 32575, Conditional Domain 2452, Adding quadratic terms improves local score but behaves unstable on LB 26592, TASK EXPLORE MERGED DATASET 10038, The captain of the ship did not survived 15429, first turn the gender values in numerical labels 21428, Make change to categorical column 17424, we are with the same 22364, Again three duplicates are in Public LB subset 35451, Imports 29593, confusingly instead of just telling the initialization function which non linearity we want to use and have it calculate the gain for us we have to tell it what gain we want to use 42327, Importing all the necessary keras library 15432, First let s start with the basic 14959, Impute missing fare values 15448, Dropping Name Ticket SibSp Parch FamSize and Cabin because they were already used for feature engineering or may be useless 20964, Importing the Keras libraries and packages 31860, we can create the bottleneck features of our training set 5886, NN 1422, To check how good each model is I m gonna split dataset to train and test dataset and use Accuracy Score from sklearn 4441, saleprice 39389, Compute feature importance 35142, Compile the model and fit to training data 40857, Categorical andNumerical Variable 10915, Encode and extract dummies from categorical features 33265, Evaluate Model 5020, Transforms 7416, TansformerMixin can be used to define a custom transformer dealing with heterogeneous data types which basically contains two major parts 24358, The most important errors are also the most intrigous 38165, TPOT Tree Based Pipeline Optimization Tool 32186, For submissions 23459, Hour 11861, Final Model and Submission 13687, Fixing the missing data 26564, This rotation separates one of the histograms but collapse all the others 31824, Python imbalanced learn module 23273, Ticket 29054, Contrast stretching 7591, Scatterplot colors SalePrice vs all SF and OverallQual 39079, Simple Blend of Predictions 25889, The Flesch Kincaid Grade Level 38984, 3 Models Vs 1 Model 14962, Save cleaned version 12623, Random Forest 32091, How to extract items that satisfy a given condition from 1D array 6305, XGBoost 17396, Parch Children Parents wise Survival probability 20045, Check missing values and outliers from data 13869, One hot encoding 311, Missing values 32072, Parameters used 36723, here are what we want to modify 25658, The largest components have over 100 nodes and 1000 edges 32227, One Hot Encoding 27363, adding category id to mean encode it 37452, This code is lifted from kernel baseline starter kernelTraining 11124, Training Evaluation and Prediction 7120, Basic summary statistics about the numerical data 1360, The perceptron is an algorithm for supervised learning of binary classifiers 6472, Evaluate ensemble methods 9344, Build model and predit 6443, Outlier 6922, Check Accuracy 24594, DATA VISUALIZATION 40870, Optimize Kernel Ridge 37673, Confirm that the train set doesn t have any overlaps with the test and validation sets 31424, Subcase x start x end cap x start x end varnothing 12720, The league table is out and we have a winner in Title 33701, Range Slider font 32036, GridSearchCV also returns best parameters and best score The output below is formatted to 4 decimal digits 27543, Display heatmap with shape size by count 38576, Random Forest looks best 31436, it s time for you to define and fit a model for your data 20262, Number of iteration is 1001 3390, Important step as Data Scientist is to work with Data 15541, Average All Predictions 26447, Regarding the false predictions there is a majority around Hence cases where the information on the passenger was not conclusive enough to make a desicive prediction By further feature engennering or by improving our model it might be possible to predict some of this cases around right 10087, Dummy variables 39191, Codificando os r tulos 2919, Split into train and test dataset 11824, Lets find out highly correlated variables as it smells like something unexpected is cooking 39103, Count Vectorizers for the data 30481, Passing a df directly to sklearn 37882, Ridge Linear Regression 23979, This part is taken from abhishek great kernel 26293, More Training data 27411, backward propagation 36560, Compare Models 34798, The variability between the actual values and the predicted values is lesser than the linear regression 1420, I m cutting Age variable to 5 equal intervals 1985, Training and Predicting 34636, Train test split 13798, Analyze by describing data 39750, Random Forest Model 19327, The distribution of data across the classes of digits is pretty much the same 8240, Seeing missing value percentages per column 37492, Reuse Embeddings from TFHub 38835, Visualize predictions 37757, you may wonder what happen if we call next past the end For this we have a built in exception type called StopIteration that automatically raises once the generator stops yielding 29520, font size 3 style font family Futura b style color green Creating data clean function 10449, LASSO Regression 4671, We have 37 numerical features and 43 categorical features Since we have a lot of categorical features and a regression problem we have to pay attention to multicolinearities after the categorical encoding 25270, Compile model 42106, Data Augmentation 39678, Using 1 we select the final image produced by our training process we can use 0 1 etc to view images from earlier in the training process 36873, Keras 3 hidden layers 34842, Early Stop 6737, MSSubClass Vs SalePrice 5047, The higher the quality the wider the price range Poor very poor quality buildings have almost the same price range while very good to excellent properties have a much wider range of possible sale prices 42107, Fitting the model 26190, Lasso Regression 8300, AdaBoost with Random Forest 4161, Important 2447, Pairplots 37297, Constructing Custom Stop Word Lists 6801, Predi o XGBoost 42087, we pre compute the weights of Resnet34 and fit the model 13874, Exporting the data to submit 26633, Validation accuracy and loss 6008, Untuk seleksi fitur seperti ini kita tidak bisa melakukannya secara otomatis dengan mesin karena fitur fitur seperti ini butuh sense dari manusia untuk memisahkannya 33257, Model Selection 24839, use CNN 10610, check completeness of our Train and Test data 5372, Recursive Feature Elimination RFE 29029, We replace missing values based on Title after feature engineering with their mean 13991, Convert Sex and Embarked to Numerical values 27383, Feature selection 19380, Fourth try 5407, In terms of age most of the people under 40 survived 20971, Import the Test Data 22051, As just one row 314 of the data contained missing values in place dropping of that row is ok 19063, For testing purposes we only use the first 100 images in the test set 4428, It is clear that the skewness really improved and that be enough for now 4223, One Hot Encoding 29887, Resume 38589, Submission File 43276, Gerando o output do modelo 21089, Determine outliers in dataset 33273, MixUp data augmentation 24914, Comparing a group of countries with similar evolution Germany Spain France and the US 14465, back to Evaluate the Model model eval 30949, Define dataset class 35624, Experiment 1 8153, Ridge 17898, Here is the final table ready for predictions 805, Some useful functions 41255, Add the first layer 2236, Bivariate Analysis Detecting outliers through visualizations 14088, Test Data 32171, ANALYSING THE TYPES OF GRAPHS 34662, Item count per day distribution 5097, apply a deep feature synthesis dfs function that generate new features by automatically applying suitable aggregations I selected a depth of Higher depth values stack more primitives 7632, blend 3 gscv Ridge gscv Lasso and gscv ElaNet 22867, Convolution Layer 42306, K Nearest Neighbors 4911, Analyze others as well 528, But survival rate alone is not good beacuse its uncertainty depends on the number of samples 1634, Converting categorical data to dummies 41861, Tokenization and Features Engineering 29879, See my posts about this issue Punctuation marks repetition in incorrectly classified text getting started discussion 166248 26663, POS CASH balance 2299, Pandas Take a look at the column names of all the fields 19830, M estimator 28182, Text Classification 18388, Calculate the selected text and score for the validation set 2825, fitting the model to the training set 38171, The arguments used in structured data classification class 39968, More passengers embarking from Cherbourg survived than Queenstown and Southampton 33779, Plotting the Train and Validation Curves 10563, Create Estimator and Apply Cross Validation 11293, look at a correlation heat map of the variables we have so far 41078, LSTM 35377, Dropping Less Important Features 23264, Missing Value Treatment 38164, Saving the Leader Model 29173, Check for skewness of features 16107, Create FareBand 34405, Load the test data 40201, Classification Report 15009, If we encode sex and look at the correlation with Survived we confirm that these variables are highly correlated 3279, Apply log tranformation to target variable and Transform categorical features values into dummies 11258, The predictions on the validation data be stacked together to create the train set for the final model 26555, Split the data into training data and validation data for validation of accuracy purpose 1302, Observations 5659, Basic info of Test data 29723, All the models follow a similar trend for Validation Accuracy 144, I admire working with decision trees because of the potential and basics they provide towards building a more complex model like Random Forest 29158, MiscFeature Fill with None 28597, Architectural Structural 1297, skew data makes a model difficult to find a proper pattern in the data that s why we convert skew data into normal Gaussian distribution 11051, Parameters of the transformers were already tuned using gridsearchCV 28948, We can check feature importance below 29145, Decision Tree visualisation 32597, Distribution of all Numeric Hyperparameters 16114, Please note that the accuracy score is generated based on our training dataset 4798, We have created flags for different feature of the house to check if it is available or not 36622, Encoding train labels 30184, Most of the learning algorithms are prone to feature scaling 35933, Embarked 30846, Crime Distribution of Districts over the Year 12930, Visualizations about training set 11246, method from house price predictions notebookEnhanced House Price Predictions 3426, Make indicators for imputed values 13619, Test Set 8749, Train Scores 222, Libraries and Data 2446, Spearman correlation is better to work with in this case because it picks up relationships between variables even when they are nonlinear 18931, Relationship between variables with respective to time 19816, Sum Encoder 10165, Box Plots 16935, First stupid models and evaluation 15917, Identify Roomates by Ticket 25434, Normalization 749, AutoEncoders to the rescue 12017, Adaboost is also a boosting ensemble model but it based on the feedback from previous model it uses the same model at each step giving more priority to the error made on the last step by this model 10561, Handle Missing Data for Categorical Data 2146, In other words houses with that particular exterior 36052, EDA 24389, Fill the other values with None 1614, Conclusion 32115, How to compute the mean median standard deviation of a numpy array 41348, There are lots of features correlated with SalePrice 28641, WoodDeckSF 16871, Pure survival rate 38889, Defining the Neural Network 14131, Cabin 1239, Training the models 2279, Feature Importance 30461, Creation of a tokenize s function permitting to automatically tokenize our train and test set 27997, Funny Combine 15509, There is no corresponding value for Cabin so let s look at the relation between Fare and the three features 40941, Outlier s Check 36433, Submission 33851, Univariate analysis of feature word common 38985, Reading Data 37034, Does shipping depends of price 26343, Variables associated with SalePrice 40026, Test image statistics 4221, Normalization 28698, Training 38557, Looks like there are no missing values in the dataframe 39139, Regression with Gradient Descent 14108, Highely correlated columns both negative and positive correlation 20698, Running the example fits the model and saves it to file with the name model 29172, Split back to train and test 10232, Here also Age is missing let s fill in the similar way how we did it for training data 21478, Test Data 27506, Building the Convolutional Neural Network 24878, we should be having a cool nice dictionary of titles and their average ages 3726, Un comment the below code to generate csv file 510, Random Forest 36581, let s take age into account 32749, Function to Convert Data Types 38789, Merge these new predictions back into the full prediction set 42216, A third convolutional layer now be added this time with a larger filter size combined with smaller kernel size 32034, So mean test std test all have similar length 33085, Label Encoding 35669, Filling Missing Values sec 26405, Most passengers travel as singles 10078, go to prepare our first submission Data Preprocessed checked Outlier excluded checked Model built checked step Used our model to make the predictions with the data set Test 3465, Here s the correlation heatmap for the predictors in the training set 32187, Convolutional architecture Conv Conv Pool Dropout Flatten Hidden Dropout Output 29155, BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF BsmtFullBath and BsmtHalfBath Fill with 0s 35198, Modelling with Linear Regression 66, After loading the necessary modules we need to import the datasets 863, Passenger Class and Sex 42753, We are including 13 features out of 16 categorical features into our Embedding 10406, View correlation of data 7820, Stacking 39701, Comparing the output of Lemmatization on non POS Tagging and POS Tagging output 8249, Random Example 34933, Best number Truncated SVD is 36060, Encoding 36119, Create some new features 22456, Density curves with histograms 28781, I was not able to plot kde plot for neutral tweets because most of the values for difference in number of words were zero 27531, Display the variability of data and used on graphs to indicate the error 37785, Visualizing sample of the training dataset 42772, Trying Random Forrest 19126, Scaling 20498, CSV Files 19589, shop and item category id 6494, Training and Testing the Model 38257, we have 7613 tweets in the train set and 3263 tweets in the test set 22174, We have 4 categorical features in our data 11675, It looks as though those that paid more had a higher chance of surviving 31703, Here we dropping some unnecessary features had their use in feature engineering or not needed at all Obviously it s subjective but I feel they don t add much to model we one hot encode the categorical data left so everything be prepared for the modelling 20313, The following table depicts the percentage these goods with respect to the other top 8 in each cluster 4599, Fireplace 27423, Top LB Scores 29717, Numerical and Categorical Features are processed differently 3574, Visualization Object Variable 20805, Some features are the wrong type so we convert them to the right type 41546, I think the model was struggling with classifying the 1s and 7s du to rotation so i removed rotation 2180, Data preparation 5896, label encoding 819, Find columns with strong correlation to target 38950, Validation Strategy 5711, SaleType Fill in again with most frequent which is WD 13037, Title 1311, Observations 25831, EDA ON TWEETS 18741, MAX NB WORDS is a constant which indicates the maximum number of words that should be present 2515, Decision Tree 15229, Label Encoding 19921, Properties Dataset 30843, Lets form a pie chart to plot the data 3144, Data Visualization 33528, Below is the unpreprocess version just for comparison 37795, Saving the scores to the submission CSV 18719, save our model s weights 3394, SalePrice Correlations 3988, Imputer with mean strategy 13339, we can group the letters with small value counts based on their survival rate 594, We learn 13586, Running the HyperOpt to get the best params 41648, Enable Parallel Processing 21529, Scale the dates and remove duplicates in order to get clearer graphs 37155, We have applied max min normalization to the values of the filters to make it easy to visualise the filters 34705, Shop item active months 14508, Visualisations 7616, fit intercept boolean optional default True 14848, Looking at the distribution of the titles it might be convenient to move the really low frequency ones into bigger groups 11818, SalePrice and GrLivArea 14778, Women are more likely to survive shipwrecks 9149, I took the average of BsmtFinType2 values within 1 SD around the BsmtFinSF2 value 14510, Passenger Class 28342, Analysis based on NAME CONTRACT TYPE 15219, Model XGBoost 16726, fare 34790, Feature Engineering 13387, Make additional variable travel alone 6158, Make a Catboost cross validation 15538, Create submission file and submit 11220, Show new predictions 19271, CNN LSTM on train and validation 24801, RNN 14281, Missing value identification handling 10930, Directed graph 7025, Electrical system 23214, important to us are 19656, Relationships 5942, create our LGBMRegressor Model 36041, Plotting the Confirmed vs Fatalities trend for top 8 countries 32306, Remove outliers 6933, After this we can pick out features which have one dominant value in the whole sample 34660, Filling it with the mean price of this particular item 2281, Training random forest again 39763, Here we ll try to find wich topics appear more often in sincere and insincere questions 7262, Parch Feature 27846, Predict and save to csv 23851, Till here I was in 20 on private leaderboard but after hyperparameter tuning I landed in top 2 16970, localise those outliers 230, Model and Accuracy 20658, look for data which is useless 1323, Which features are mixed data types 2803, display missing Display missing values as a pandas dataframe 20459, Number of family members of client 41982, len to check the length of the dataframe 16032, Same as Cabin we create dummies values 2326, Using RandomSearch we can find the optimal parameters for Random forest 8868, look at our regplot for GrLivArea vs SalePrice again 27752, Common stopwords in tweets 24259, 3 types of deck 1 with 15 passengers 2 to 6 and 7 to 8 32606, Corralation between features variables 27308, Display images 40848, Outliers Treatment 17858, Train the first level models 7650, label encode some categorical variables 38827, Define MLP model 16572, Random Forest Classifier 7614, Preprocessing Scalers 21658, Dealing with missing values NaN 14591, Observation 42129, Train top layers 14104, center Boxplot center 18642, here again we have 27734 missing values 40043, both groups look somehow far away from the others 8880, Feature Engineering 42123, Predict 14102, center Histogram with hue center 16467, Spearman s 18608, Parameters are learned by fitting a model to the data 11965, firstly finding columns in dataset which contain null values 913, LotFrontage is remain to impute becuase 14374, Majority of the passengers Embarked from S and almost all of the passengers embarked from Q were of Pclass 3 this was also a reason of less survival rate of passnegers embarked from S and Q as compared to C 3931, Save submission 11109, Model Testing Only Numerical Features 41794, we are done with our analysis 32848, Time period of the dataset 38255, Predictions 18037, Make predictions 5127, xgBoost Classifier 2880, Using Xgboost for the Classifier 3539, Categorical Features 2473, Making AGE BINS 9517, Sex 32672, Outlier removal 38777, Save predictions before small improvements 12051, Decision Tree Regressor 7397, I split the analysis based on the different types of variables 35880, Derive some time related features 40086, Adding missing columns with zero values 6901, Cabin 6580, Log Transformation 36289, Linear Regression 24028, It is also oblious that different catgories and shops have different seasonalities lets look at it first 21790, Missing data 29741, Number of Kfolds 37712, Set rest of the things 27962, Load packages 27908, Remove Null Value Columns 19973, The model needs to know what input shape it should expect 2537, To rescale our data we use the fonction MinMaxScaler of Scikit learn 34766, Creating Submission file 20482, Credit overdue CREDIT DAY OVERDUE 7854, Scientific ish Feature Analysis to Improve Random Forest Regressors 26852, Meta Feature Engineering 12968, Pclass Sex and Survived 34043, Check if there are any missing values or typos 499, Sex Feature 21793, Support Vector Machine 43315, Age is normally distributed 42299, In order to avoid overfitting problem we need to expand artificially our handwritten digit dataset 11055, There are many ways for filling missing values just filling in the median of the values for now 22804, look in detail the numerical features that need to be dropped as they do not contribute to the primary education 35786, Use another Stacking function from mlxtend 5733, Stacking Averaged models Score 18833, Ensemble prediction 23697, Confusion matrix doesn t look so bad 8902, Random Forest 2791, Tune model 18837, Visualizing the Eigenvalues 23936, Text exploration 35846, Lets create prediction on RF Model 1866, Best Model ElasticNet 35130, Adding additional features that records the avg 38163, lets check the performance in our test set 18454, Converting Text Data font div 18690, create our learner by specifying 26892, Include numerical columns One hot Encoding 29026, look for missing values 12323, We judge it as outliers and remove it 4435, lets check the loss plots 43372, Defining Convolution and max pool function 1679, SpiderMan Ah interesting A numerical feature with big values 36970, The Accuracy of this model on kaggle leaderboard Quite Reasonable Score for so much HardWork 25297, I really want to find features that generalize well but this is just random noise 29148, Take a Peek at the Dataset 1594, When I googled Stone Mrs George Nelson Martha Evelyn I found that she embarked from S Southampton with her maid Amelie Icard in this page Martha Evelyn Stone Titanic Survivor titanica org titanic survivor martha evelyn stone html 36256, Hehe null values spotted 31266, The below diagram illustrates these graphs side by side 2340, Introduction to Feature Importance Graphic 40161, All store types follow the same trend but at different scales depending on the presence of the promotion Promo and StoreType itself 23810, Loading ML Packages 225, Library and Data 21571, Combine the small categories into a single category named Others using frequencies 18197, distribution of returns by product 12648, Expand some of the Test Features 12613, replace NaN by 0 31268, The below diagram illustrates these graphs side by side 28197, Lemmatization 11038, Choosing the estimators 36066, Fit Model 140, Gaussian Naive Bayes 1343, We can not remove the AgeBand feature 21923, Support Vector Regression 20185, Pandas profiling Report analysis 24323, Label Encoding three Year features 11366, Since area related features are very important to determine house prices we add one more feature which is the total area of basement first and second floor areas of each house 23657, Rolling Average Price vs Time storewise 13908, How many people in your training set survived the disaster with the Titanic 13994, Impute Age using median of columns SibSp Parch and Pclass 29443, One important thing we have to check is whether or not our test and training set are from the same dataset 32474, Data Subsetting 24911, For day to day track of this COVID 19 Cases Deaths please refer my another notebook 9422, Pie Chart 2801, describe Calculates statistics and information about a data set Information displayed are shapes size number of categorical numeric date features missing values dtypes of objects etc 5526, Estimate missing Age Data 8530, Electrical 3900, Leave One Out Encoding 29893, Fit the prediction model again using the new weights 25995, Viewing Tables 34266, Multi Dimensional Sliding Window 33334, FEATURE 7 RATIO OF CASH VS CARD SWIPES 19451, Data Preprocessing 15500, Sex 19570, Preparing for training 27464, Distribution of target variable 14581, Age 37643, Street address and Display address 13952, Find Missing Value 12049, Modeling 5444, For simplicity lets choose only a few features 38843, Mostly 1 Story houses followed by 2 Story houses 33706, Gantt Chart 20119, Data Preparation 23520, The text after the cleaning processing looks like this 7103, let s do grid search for some algorithms 37369, Predictive Modeling 34967, Assigning 1 to females and 0 to males 31254, Modeling 15479, We drop parch sibsp and family size as correlation coefficient is lower than IsAlone and create noise 39861, we can look at how the prediction values are spread 40019, File structure and dicom images 35651, Light boost font 10933, For the degree centrality measure the normalized interpretion is really intuitive 10527, Misssing Values 39866, Label Encoding using LabelEncoder 41495, There are 52 duplicated rows 4330, Marital status does not apply for those who are not of legal age 19823, Define a binning function for categorical independent variables 15803, center Decision Boundary Visualization center 26264, Predicting over test set 21667, WBF Ensemble methods 1252, Fill missing values 19267, Comparing models 8484, Linear Regression 34717, Data preparation 10537, We had discussed about some numerical features which looked like categorical features 15949, Spliting the train data 16577, Visualize data 8777, Survival by Embarked 15545, Processing NaNs 30151, Modeling 9674, For this baselien model we are going to 11669, Visual Exploratory Data Analysis EDA 38146, ML BOX 12897, def mapSex 24939, The lognormal distribution is suitable for describing salaries price of securities urban population number of comments on articles on the internet etc 10631, KNeighborsClassifier 28825, Example 2080, Indeed upon adding the feature Boy the CV score increases to 0 15504, Ticket 2741, Checking for Missing values numerically 35376, Inference Model 13486, LOAD DATA 309, finally the submission 4167, Reciprocal transformation 13678, Women had a much higher chance of survival than men 20519, analyze the relationship of SalePrice with other numerical variables 6339, Numerical features 42247, Addressing outliers 19448, Compared to our first model adding an additional layer did not significantly improve the accuracy from our previous model 25263, GrayScale Images 26582, The images tend to be dogs in cat like poses 25399, COUNT OF EXAMPLES PER DIGIT 10218, It s clear that female passengers with lowest fare also survived the disaster and passenger with highest fare also survived irrespective of the gender and this proves our theory that Socio Economic Status played an improtant role in survival 20500, Display Duplicates 4437, Prepare Test table 2464, Using Pearson correlation our returned coefficient values vary between 1 and 1 2154, Numbers are crucial to set goals to make sound business decisions and to obtain money from investors 25753, Removing color correlations 37539, ANN PYTORCH MODEL 22371, K Nearest Neighbors 40292, Nice 21373, Normalization 34935, Drop columns with high correlation 39426, use one hot encoding for Embarked since those are nominal variables 7923, To correct these feature since some of them have high skewness we re gonna use a generalisation of the np 6770, Submission 28429, Item info 42280, Model 1196, fit our first model 7729, These features are not much related to the SalePrice so we ll drop them 24109, Analysing and Saving our Model 42764, Age 8986, I am also interested in comparing PUD homes verses not 5080, Several Kagglers regressions top 4 on leaderboard apply a box cox transformation cox html to skewed numerical features Cool idea worth persuing too 22621, Some helper functions to facilitate training and give a nice overview of the 2 dimensional latent space are defined 35363, Creating evaluation set 31262, Load the data 29771, We identify the predicted class for each image by selecting the column with the highest predicted value 13672, Here train columns docs stable generated pandas DataFrame columns htmlpandas DataFrame columns returns all the column labels in the train DataFrame and we then use values docs stable generated pandas Series values html highlight valuespandas Series values to get an easy to print numpy ndarray 14309, Creating the Model 13714, 1st METHOD THE ONE I GENERALLY DO 6377, let s calculate range variance standard deviation and find out and visualize the quartiles 13278, Logistic Regression is a useful model to run early in the workflow Logistic regression measures the relationship between the categorical dependent variable feature and one or more independent variables features by estimating probabilities using a logistic function which is the cumulative logistic distribution Reference Wikipedia 15117, Logistic Regression Model 35454, We cannnot fill this diagnosis column because it may affect other variable too 3884, Numeric Attributes 21344, Here is my optimal parameter for LightGBM 5429, Electrical 40022, load an example 39452, checking missing data in bureau balance 42991, After we find TF IDF scores we convert each question to a weighted average of word2vec vectors by these scores 25893, Linsear Write Formula 17702, MODEL COMPARISON 39296, TARGET ENCODED FEATURES 3600, Average 211, Model and Accuracy 34175, Visualizing Layer 2 23647, Run 5 Fold Training 34396, Training Models 3541, Categorical Feature Exploration 22440, Marginal Boxplot 6650, Categorize sex to numeric variable 14647, Separate train and test data 38853, Training a model is like teaching a kid 19440, Creating Train Function 36130, In our example we have images with 98 4902, let s have the Training and Testing ID s aved in a dataframe for future references 24948, We must not forget that this is not a silver bullet again it can make the performance worse 36604, Competition Submission 13731, OHE features Sex Embarked Title Pclass new 26378, Images That Confuse the Network 26759, Cleanup 14938, Missing Values 22028, Clusters 16616, Categorical Variable 15599, Survival by Age Port of Embarkation and Gender 23985, We take log as logs are used to respond to skewness towards large values e cases in which one or a few points are much larger than the bulk of the data 21, RandomizedSearchCV Linear SVR 19904, Lag Feature are being created 16601, Realtion Between Variables And Survived 24814, data engineering 40680, let s visualize how these cluster center look like in the original high dimensional space 28216, PPS matrix of correlation of non linear relations between features 18102, We train all layers in the network 31293, we create a function that calculates the period of a data series 34619, Multilayer Perceptron 37215, good coverage but the top missing words all have contractions 35056, Making predictions using Solution 1 25885, Histogram plots of number of words in train and test sets 31695, this is how we gonna fix most of the missing data 25469, Plot graph of cost 23583, Make predictions 32717, Transformers 26, GridSearchCV ElasticNet 34180, Interpreting CNN Model 1 35148, Experiment Number of convolution layers 23426, First let s check tweets indicating real disaster 30114, The learning rate determines how quickly or how slowly you want to update the weights 20610, Cabin 33573, Download and Import Dependencies 27491, MODEL TRAINING 16099, Sex Feature 8151, Random Forest Regressor Model 2683, Univariate roc auc for Regression 7076, Extract Title from Name 43098, create the embedding matrix where each row corresponds to a word 21383, Our goal is to avoid overfitting 18745, First lets remove rows with null values in column Age SibSp ParchFare 14910, Refill the missing values for new Embarked in both training and testing dataset 20859, Test 31855, Add month and days in a month 32303, Display heatmap by count 8005, SVM Linear 38289, Dealing with nulls in MSZoning 20496, Train Test Image Names 8002, Try to boost with gradient method 14330, The Ticket column is used to create two new columns Ticket Letter which indicates the first letter of each ticket and Ticket Length which indicates the length of the Ticket field 11437, Ordinal Variables Mapping values according to a correct order 29849, Getting dummy other categorical features 20934, Make a prediction about the test labels 16830, Metrics beyond simply accuracy 27233, Choose type for each feature 18089, look at the most yellow images 36720, Prepare the Dataset 14301, Applying Feature Engineering 29839, Association rules function 26294, Data Preprocessing 17525, We can further divide the cabin category by simply extracting the first letter 12348, BsmtExposure Refers to walkout or garden level walls 36610, Labelling the test data 28039, Same to the process we had done in countVectorizer we need to tokenize the text and convert them to list of integeres 20553, Accelerator initialization 34357, Our model is having trouble identifying the people with class 1 4052, Age Analysis A Simple Approach 40677, try also for K 16 27367, adding the year and month 28264, We shall use strip plot along with boxplot as box plot alone not give us clear cut idea about distribution of data points within the box thus increasing chances of losing some relevant interesting patterns among two features 15280, Survival by Age of passangers 29910, Distribution of Scores 39016, Replace null age values for the average age 11808, We are ready to fit our models 7043, Proximity to main road or railroad 21818, Bathrooms 29386, 2nd convolutional layer span nbsp nbsp no of filters n2 32filter dimentions 5x5 we use the relu function as the activation function 42944, Predicting 7118, Importing Librarires 35609, Predicting 9713, check homoscedacity and uniform distribution of residuals with fitted value 5000, Here s the smallest house 40297, Target Variable Exploration 23511, There are 2 elements in the class 26518, Renta spread in each Cod prov 35573, Combined Sales over Time by Type 16984, XGB Classifier 26438, First of all we define the function create model which gives us the freedom to try different model architectures by setting the respective hyperparameters 5510, Feature Selection 42796, Getting the best threshold based on validation sample 18600, There is something else we can do with data augmentation use it at inference time 37567, Missing values 42627, Difference between Lockdown Date and First Confirmed Fatality 11718, SVM 12063, Exploratory Data Analysis 26560, Get the parameters 19538, train time series df could be saved for later use as it is the basis of a lot of manipulations 38124, Check for data type via 19164, if item description present yes no feature 26881, Create Submission File for approach 2 22914, We then pick up the 1133 selected features to do Grid Search CV to find optimal hyperparameters 27253, XValidation 4479, We use cross validation to test our models 23603, The iterator returns a yield of a TaggedDocument every time the Doc2Vec 13111, Decision Trees with AdaBoost 9991, PoolQC data description says NA means No Pool That make sense given the huge ratio of missing value 99 and majority of houses have no Pool at all in general 25980, Holdout Prediction 41256, Add the remaining layers 7990, Dropping very null attributes 215, Gaussian NB 9907, Ensemble Modeling 15049, With a large family size may lead to a big burden in unexpected situation 41679, lets also take a look at some images from the test set 10170, This indicates data is in equal proportion 26887, Create Submission File for approach 4 25733, Train our Network 30315, Hugging Face Transformers tokenizers must be prepared in your Kaggle dataset to use in off line notebook 26464, We first split our training data into training and validation set 38178, Tune a Model 13035, Pclass 6895, Age 2013, In our previous example we could tell that our categories don t follow a particular order 20840, we ll demonstrate window functions in pandas to calculate rolling quantities 33476, Implementing the SIR model 16086, Sex vs Survival 6371, Averaging Regressors 35113, Training Evaluation 6071, Taking care of the truly missing data 27986, Partial Dependence Plots 33320, Prepare dataset for training model 36614, Calculate covariance matrix S dxd 8701, Lastly checking if any null value still remains 21207, He initialization 31626, LSTM models 5238, Imputing Missing Values 14420, go to top of section prep 4623, Correlations between Attributes 1841, Log Transform SalePrice 42392, How do sales differ by department 20530, Fix skewed features 13867, Extract new feature after mapping 16547, Here s how you can fill missing ages using the Pclass column as reference 23468, Using different models for registered 24565, Total number of products by seniority 13768, take a break with interractive plots and come back to our good old seaborn So how many passenger survived 26064, we ll plot the outputs of the second hidden layer 19251, find out what s the time gap between the last day from training set from the last day of the test set this be out lag the amount of day that need to be forecast 19751, Model 21729, we are going to plot the price distributions of the top 10 categories excluding their outliers 10698, Training set 27425, Distribution of Scores over time 7653, convert categorical variables to dummy variables 38500, The text and selected text column have one row missing each 25798, The Pareto principle e 21174, Displaying original Image 828, Convert categorical columns to numerical 21499, Load training and testing csv files containing image filenames and corresponding labels 19182, Generating submission 4012, you can use our trained model for forecasts 37762, Technique 8 Do not use operator for strings 20195, I have tested for some but you can for other features 36580, We merge this individual app usage data frame back into the gender age data and determine the most frequently used apps separately for men and women 41650, Building Stack Model 8470, Separate data for modeling 3868, according to the here most of these attibutes can be converted to scores 11977, fill all others with 0 23637, Wiki News FastText Embeddings 17659, Observations 30654, At least bioterror bioterrorism annihilated annihilation and blaze blazing mean the same 26628, Compile the model 32173, REVERSED FEATURES EXAMPLE 10423, LGBM 16710, Correlations with each feature 32014, For Name column we assign a new category to all rows The names containing Miss are assigned Miss as the new category Same is done for Mrs Ms Master and Mr To differentiate Mr and Mrs we use dot in substring search These abbreviations end with a dot most of the time but there is one row with Mr without dot we incorporate in code Mr Mr code meaning code Mr code or code Mr code 7130, Age 28497, Upsss 27557, Second attempt use an OOV token 17285, You can select several rows of dataframe by indexing 24573, Observations 34646, Predicting For Test Data 24951, Train set 35660, Categorical Features 22783, Lineplot with rangetool 30581, Applying Operations to another dataframe 3150, model accuracy improve by 1 with randomforest 34684, Adding test shops items last month 26899, Create Submission File for approach 8 32068, visualize the transformed variables by plotting the first three principal components 26703, Plotting Sales Ratio across the 10 stores 16289, Public LB 866, Embarked Pclass and Sex 34350, PyTorch Fast ai 22159, We assume that 24994, Selecting best numerical variables 27345, removing high and negative prices 3272, Fill these BsmtCond null values with BsmtQual and others with None as they have no Basement at all 42003, Multi Column filtering 21766, Renta missing values 11006, I am going to plot histograms for Age attribute to find which age group survived the most 7738, feature creation 3157, The factorize function converts categorical features into ordinal numbers 40168, The next thing to check the presence of a trend in series 29439, Location 14718, AND THE WINNER IS 40441, Plotting Loss of the traning model 19854, Outlier Detection Removal using IQR Inter Quartile Range 42803, Our target variable is spread across few orders of magnitude it s more suitable to work with log10 of this value 13403, Visualize feature scores 39192, Codificando outras features categoricas 20201, Numerical Features Data correctness 19613, Interaction features 43011, Feature Assembly 7403, After we apply the log transformation to LotArea the skewness is reduced a lot but it is still not a normal distribution based on the test 17865, We fit the model 15031, Embarked 23933, Price distribution 37996, Here we have information about item characteristics and sales for each day 10919, Displaying nodes 4602, SUMMARY 19579, Categories Analysis 27030, The smaller the prob1 the more likely a sample is positive 23235, Test different parameter values 41422, Deploy the chosen model on test set 27020, Making the Submission 41121, Los Angeles Average Absolute Log error 292, The majority of passengers with an assigned Cabin survived 41942, We ll also define a helper function to save a batch of generated images to disk at the end of every epoch 7048, Roof material 30369, Test Imageio 11509, Kernel Ridge Regression 33754, Adding categorical features 41608, Predict the test images 4484, Support vector classifier using RBF kernel 33282, Data Merger 15356, Here we have a couple of plots of the interaction between some of our features on the side of each plot we have a scale of feature importance 35674, Mapping Ordinal Features 5259, In the next series of experiemnts we are going to train a number of RF models that use only top 50 features as ranked by RFE feature importance feature selection algorithm 24710, Train The Gaussian Model also known as PCA 30544, Feature Engineering Previous Applications 39500, The number of rows and columns we have in the application train csv 42137, MNIST 12835, As we created new fetures form existing one so we remove that one 5451, Sklearn 32960, cont1 and cont10 cont9 and cont6 cont10 and cont6 are highly correlated 43403, I am doing a stratified split and using only 25 of the data 9685, Exploratory data analysis 936, Load Data Modelling Libraries 15573, Ticket group survivors feature 3902, Topics covered in this tutorials 22811, Summary Total 6465, Pipeline for the categorical attributes 43245, Splitting data for training and testing 20285, Survival Rate for chlidren are pretty good 22777, Plot a graph to trace model performance 6902, Family Size 40823, Time to merge tables 36202, html Predictions font 11904, Model training 40778, Script for adding augmented images to dataset using keras ImageDataGenerator 42842, US 2016, Fixing skewed features 19379, LinearRegression array 15195 10227, As this column consist of only two values let s encode this with 1 for feamle and 0 for male we can use hot encoder also but for starters let s avoid that as we have very simple column to encode 5069, Cool Much better Already sth around rank on the Leaderboard with just one feature 19933, Parch 3641, Filling in missing values in train set for Embarked 19317, Evaluation prediction and analysis 2636, check unique values for each column 2742, Missing values are frequently indicated by out of range entries perhaps a negative number in a numeric field that is normally only positive or a 0 in a numeric field that can never normally be 0 31090, BsmtFinSF1 font 39004, Implement the forward propagation for the model LINEAR RELU LINEAR RELU LINEAR RELU LINEAR SIGMOID 38665, Splitting the Dataset 21394, Creating Train and test dataset 18064, BernoulliNB Model 10128, Logistic Regression 20345, We can inspect the ImageDataBunch as 32622, Token normalization 38947, Reading in the dataset 1827, Elastic Net 4166, Logarithmic transformation 38659, Embarked Ratio 17672, Womens have more chance to survive 18744, Vanishing Gradients Problem 40490, Best 14493, logistic Regressor increased by 1 29021, check the correlations between our numerics target 33647, There are a few columns with different set of categories among train and test sets 36335, Build the model 26953, Import necessary libraries 24054, Dropping features with one value Utilities Street PoolQC 17803, Similarly we map Age to 5 main segments labeled from 0 to 4 3026, Separating the training and the testing set 4555, Filling value for MasVnrType and MSZoning with most repeated values 43034, Reshape the Data 17538, Encode some features 43274, Fazendo previs es nos dados de teste 29841, Numerical data 28088, Train 38826, Define the PyTorch dataloaders 18713, save our model s weights 26586, TASK IMPORT SALES TRAINING DATA 12456, we take care of BsmtHalfBath and BsmtFullBath 19404, Training the Model 21158, we fit the model on all training data we have 36044, Cleansing the data set 17355, In machine learning naive Ba classifiers are a family of simple probabilistic classifiers based on applying Ba theorem with strong independence assumptions between the features 29973, Training corpus using BertWordPieceTokenizer method 4770, Cabin column is dropped because there are a lot of na present in them 32694, now generate a submission file according to the instructions 13859, With the number of siblings spouse and the number of children parents we can create new feature called Family Size 32741, FS by the SelectFromModel with Lasso 41738, We start with frozen weights and then unfreeze them for some more tuning 4665, Correlation matrix in Visulization Form 13598, Ordianal Encoding 37102, Univariate Selection 18461, The following code uses some tools from keras to Tokenize the questions text ie assign numbers to every word 9595, Finding out people with age greater than 70 in the dataset 1669, We have to transform our categorical to numeric to feed them in our models 15711, Gender by Fare Categories vs Survived 8069, Outlier 2669, Correlation 32832, Pre training BERT 3733, True there are missing values in the data and a quick look at the data reveals that they are all in the Age feature 20041, we can test our model 7040, Flatness of the property 13996, Impute Fare with it s mean 31295, Plotting the macro data 40937, Start Searching 15436, let s start having a look at ensemble learners 42969, compare the accuracies of each model 38140, Plot the cumulative explained variance as a function of the number of components 18344, Age of house sold 8792, Skewness 10209, try to understand each features in training data set 29692, Predict Put input values throgh model and get output 36111, Handling Missing Values 7636, Blend Ridge XGB 21603, Fixing SettingWithCopyWarning when changing columns using loc 26622, Images samples 32608, Categorical Ordinal Features Exploration 6758, Test set 11614, Cut unwanted feature 34617, One Hot Encoding 30471, Create polynomial features 15112, In preparation for our modeling we convert some of the categorical variables into numbers 785, Kernel Ridge 19588, item category id 28324, identifying the missing value in credit card balance dataset 7780, Predict Test Data 22909, Logistic Regression 6925, Prevendo os pre os da casa utilizando o modelo de Regress o Linear 8556, Navigating DataFrames 2262, Missing Data 8813, for each passenger I ll just try to create a new feature called Deck with first letter from the Cabin as its value 21229, The following cell returns the number of images we have in each dataset 4078, Relationship with categorical features 20054, Create Lag Features and Mean Encodings 18074, plot some examples with small number of spikes per image 35809, Simple train dataset 1523, And finally lets look at their distribution 18637, We can now convert the field to dtype float and then get the counts 34336, Simple understanding about the data 2010, Imputing Missing Values 27509, Know This is Wrong Method of collecting data from keras model but this is also an research on data 37659, Flatten predictions array to get 1 D array for submission 8295, Bagging oob score True 37658, ImageDataGenerator again 10035, Univariate analysis 34722, LGBM feature importances 23239, Data Splitting 21750, Average Voting 40881, Advanced Ensemble Methods 21210, We need to normalize the values and need to bring the mean and std to zero 933, Retrain and Predict Using Best Hyperparameters 34621, Building our first model 30254, we can train our model with preset parameters and best nrounds value we had figured out by now 36420, Dropping features with huge missing values 17741, Filling in NaN s for variables with high coverage 20468, Goods price 26695, Listing the available files 37726, Feature Outlier Analysis 23654, Merging the data with calendar real dates 11833, MasVnrArea is categorical in the data but it shouldn t be 3630, check whether or not these added features are correlated to the SalePrice 30267, You can use precision recall chart to select best threshold value for your specific task 15518, And then we still need to bin the data into different Age intervals for the same reason as Fare 1354, Model predict and solve 544, double check for missing values 8924, MiscFeature 23796, Drop Id 8710, GRADIENT BOOSTING 7993, Analys labels using p value 30973, Random Search 31387, This is an awesome function I created that preprocesses the data It does thes following 835, Check for Multicollinearity 23039, check whether the trainsaction is right 32711, Making a basic LSTM model LSTM stands for Long Short Term Memory networks 879, sklearn StandardScaler 11252, Adaboost kinda sucks 22905, We try to distinguish disaster and non disaster tweets 23449, Weather 11107, Handling Null values 21850, LSTM Long Short Term Memory RNNs 31423, Case x start leq x end 24933, This transformation preserves the distance between points which is important for algorithms that estimate distance 35253, The difference between length of words and jaccard in same plot would tell us how it is baised 25665, steps 3739, An advantage of logistic regression is that it s easily interpretable 11414, Use Case 6 California Biking Radial Bar Chart 13027, Parch 43289, Um R de 0 19452, Building a 3 layer Neural Network 32813, Level 3 Logistic Regression 26945, The plot makes more sense if we remove that data point 12475, Cabin 5158, Machine Learning Models 30291, Adding some derived features 4763, the value zero doesn t allow us to do log transformations 30379, Predict on Test Set 21032, Single Predictive Power Score 18346, Quality Scores 17567, Modeling Data 23880, explore the latitude and longitude variable to begin with 14577, There are some missing values in cabins Age and Embarked in the datasets 16172, Correlating categorical and numerical features 32386, Meta Feature Importances 29004, How to create a submission csv 13131, have a look at the count distribution for Embarked 7376, Merging datasets using surname codes name codes and age 13101, Train Test split 25160, Percentage of Similar question and Non Similar Question 8764, Age by group every 10 year old 26520, Distribution of product by Cod prov 8043, Fare 34030, How to treat categorical variable 42610, Train Steps 2137, Tuning XGBoost 25006, Pulling Items Shops Feature 1750, Comparing the KDE plot for the age of those who perished before imputation against the KDE plot for the age of those who perished after imputation 2712, PCA is used to decompose a multivariate dataset in a set of successive orthogonal components that explain a maximum amount of the variance 15817, From this we can infer that women of class 1 and class 2 survived most and nearly all men of class 1 and class 2 died 678, For additional insight we compare the feature importance output of all the classifiers for which it exists 15355, 2D Partial Dependence Plots 21859, Adding Noise 17265, Fare Feature 27942, Train on the full training data 14449, go to top of section engr2 23142, Findings A large number of passengers who survived were without any siblings or spouse followed by passengers with spouse or siblings Percentage wise passengers with spouse or siblings had over chance of survival followed by passengers with siblings or spouse had over chance of survival Passengers with or siblings or spouse had all died 31608, Spliting Train and Test sets 40146, In this first section we go through the train and store data handle missing values and create new features for further analysis 31571, if you want to take data augmentation you have to make List using torchvision transforms Compose 24328, By using a pipeline you can quickily experiment different feature combinations 6816, Exploratory Data Analysis and Data Cleaning are two essential steps before we start to develop Machine Learning Models 41476, Feature Family Size 22671, Determining Topics 500, Women are more likely to survive than men 2501, Fare Continous Feature 24524, Almost all obervations have come from non employees N 13493, Cabin 11912, Age 34353, We define our learner s loss function 7965, Another pre processing step is to transform categorical features into numerical features 28370, As one can expect from any textual data there are all sorts of messyness going on here 33028, Visualizing the model s performance 6360, Ridge regression 8426, Fill Missing Values of Garage Features 35832, Now lets give an another category named Unknown for every categorical features that have missing values 34053, Import XGBoost and create the DMatrices 27969, Reducing for test data set 14636, let s fill the missing values for the Fare 11102, Split Categorical and Numeric Features 37310, Using RFE Recurisve Feature Elimination 18179, Dataset visualizations 1939, Neighbourhood 37431, Common punctuations in tweets font 38976, Data loading and batching 26105, My second shot was to take tweets from another source Luckily there is a kaggle dataset containing m tweets that can be found here 8111, Cabin Ticket 16982, Random forest classifier 15013, Siblings Spouses 29728, In this kernel I be using 50 Stratified rows as holdout rows for the validation set to get optimal parameters Later I use 5 fold cross validation in the final model fit 11475, Create Submission File to Kaggle 2923, Decision Tree 32712, Plotting training and validation accuracy 10588, Random Forest prediction with all features 36885, MLP 42849, Logistic Curve Fitting 5249, Modelling and Feature Selection Pre Requisite 23831, Taking as threshold on grounds of experimental changes 4283, GrLivArea ranges from 334 to a maximum value of 3627 ft 2 7311, Observation 37433, Which are the most common words in selected text font 13618, Train Set 3859, Logistic Regression for analysis 14589, Survival 10805, Firstly let s fill empty values 12006, Due to some negative errors here we not directly applies RMLSE instead we use RMSE 26218, References Albumentation Documentations team albumentations examples tree master notebooks 3717, GarageYrBlt PoolQC Fence MiscFeature 6037, Train Test Split Classic 34522, Time Features 136, Manually find the best possible k value for KNN 25452, Train and Predict 18393, Evaluate several models 13588, Predicting the X test with Random Forest 4398, Feature Engineering 29041, Highly skewed distribution of labels 15061, Model Feature Importance 5192, There is VotingClassifier The idea behind the VotingClassifier is to combine conceptually different machine learning classifiers and use a majority vote hard vote or the average predicted probabilities soft vote to predict the class labels Such a classifier can be useful for a set of equally well performing model in order to balance out their individual weaknesses Reference sklearn documentation learn org stable modules ensemble htmlvoting classifier 6320, Logistic Regression 31582, let s check how the target classes are distributd among the CAR continuous features 13041, Sex 41944, that we have trained the models we can save checkpoints 32409, Submission 29356, Age feature 12451, And that GarageYrBlt is clearly an outlyer 38488, Confusion Matrix font 4767, Replacing the age value in the test and train dataset with the median value of the age column in their respective dataset 24437, Wrapper 15292, Creating categories for Male and Female and dropping few uneccessary columns 2334, Understanding Feature Importance 35092, Running model on the test set 18739, Normalization 963, First level output as new features 12902, Logistic Regression 27128, There are still some missing values in test dataset as we were following train dataset 33653, Exploratory Visualization 20499, Find Duplicates 37576, Language model in action 7271, Cabin Feature 38408, Yeah we guessed 1854, Identify and Remove Outliers 1585, Data cleaning Feature selection and Feature engineering 39987, Feature Engineering 17968, Support for missing values 737, Test Set 30849, Locations of Top 5 Crimes 32500, Predictions 13450, Data Modeling 26258, Defining the layers and metrics for the Neural Network 9790, While encoding it is vital that we skip missing values 7776, AdaBoost 34643, XgBoostClassifier 42570, submit the predictions with the threshold we have just found 13075, we fill in the missing Fare 16631, Data properties 15426, Great majority of people travelled on their own 23795, Pandas Profiling is one line code to view most key information 21114, I have created optimal binning for people char 38 new variable people class 38 21591, Webscraping using read html and match parameter 10983, We are going to use The Gradient Boosting Regressor but before we need to know what the best parameter to use also we are going to need GridSearchCV for this job 21685, An lise descritiva dos dados 1118, Cabin Missing Values 2487, Age Continous Feature 29556, For embeddings we are using 4251, Discrete Features 9690, based on this we drop GarageArea TotRmsAbvGrd and GarageYrBlt 1stFlrSF 40978, It s supposed to be a DataFrameGroupBy 23531, Support Vector Machine prediction 41234, Load Data 25167, Analysing Shared Word 37676, Format data to input text Q A format 30636, The idea and approach for creating a Title column was taken from the following great notebooks p 26421, With the title Master we are able to identify young male passengers which have a significatly higher survival probability than adult men 39390, Loop over product columns assign every column to the target vector y and compute the feature scores each run 19664, Correlations with the Target 23275, Building Models 22336, coming back to Lemmatization we need to define a wordnet map and specify for which all parts of speech we need to find Base words otherwise by default it fetch base words for nouns only 16404, Using Sklearn contingency matrix 33745, Before fitting the parameters we need to compile the model first 32475, This frees up space allowing us to finish our feature engineering 8567, Dropping Duplicate Rows 21386, Predict 2577, Gaussian NB 6563, Fare 40179, My upgrade of parameters 26894, Score for A6 16073 20705, Generate test predictions 14683, this gives a very good insight about family and gender relations 34657, Revenue 34148, Just importing the necessary libraries 26954, Load Data 33350, Timeseries autocorrelation and partial autocorrelation plots daily sales 16130, Train Test Split 8313, Imputing the missing numerical values with the median value as the features are not uniformly distributed 7462, Logistic Regression Implementation 43093, We define the hyperparameters for the model 39111, Name 7777, Gradient Boosting 27924, Model Tuning 6712, Relationship between Categorical Features and Label 36140, plot the train and test scores for the different neighbour values 1802, Here we are plotting our target variable with two independent variables GrLivArea and MasVnrArea It s pretty apparent from the chart that there is a better linear relationship between SalePrice and GrLivArea than SalePrice and MasVnrArea One thing to take note here there are some outliers in the dataset It is imperative to check for outliers since linear regression is sensitive to outlier effects Sometimes we may be trying to fit a linear regression model when the data might not be so linear or the function may need another degree of freedom to fit the data In that case we may need to change our function depending on the data to get the best possible fit In addition to that we can also check the residual plot which tells us how is the error variance across the true line look at the residual plot for independent variable GrLivArea and our target variable SalePrice 11743, Imputation Feature Engineering 35607, Fitting 4106, Predict OUTPUT 27314, Graphing Accuracy 278, Were younger passengers more likely to survive compared to older passengers 42340, As we have a high dimensional dataset feature engineering be quite time consuming if I check the co variate one by one Therefore I first focus on exploring the response variable PCA might also be a good choice to reduce dimension if necessary 36430, Hyperparameter Tuning 16990, Gaussian Process Classifier 40184, The main problem is in the predicting of the longest and shortest selected text which are most or least different from the given text 18839, PCA Implementation via Sklearn 9602, Viewing the data randomly 19072, total number of survived passanger 23853, Check for output tab of the notebook and check the score after submitting it 29184, If the feature is irrelevant lasso penalizes it s coefficient and shrinks it Hence the features with coefficient 0 are removed and the rest are taken Dropping one column from the one hot encoded variables wouldn t be necessary as this step definitely eliminate one of them 33638, Extracting the ID since we need to use it for submission later 16698, Completing a numerical continous feature 32040, we compute the accuracy using accuracy score of Scikit Learn 25519, Your Average Cat and Dog Photo 24589, Generate submittion csv 4140, Training the model 14777, Sex 1945, Relationship with OverallQual 14555, Fare font 1949, Here there are many identical features which means that they convey same information so it is worthy to consider them only 1 time 15472, Feature Sex 9961, i count how many houses was sold by year station 3532, Housing Price vs Sales 31532, Handling Missing Values 37450, the comment text is prepared and encoded using this tokenizer easily 39019, Drop no necessary columns for the data analysis 16958, Fare 34446, Exploratory Data Analysis 14317, Data Defination 12392, Splitting data into train test sets 14417, Statistic Summary CATEGORICAL data 4733, there lots of columns have null value 42342, Check whether the response variable is normally distributed 12445, Submission 10752, Use log 1 SalePrice 40331, Build word wectors from text field 9054, TotalBsmtSF 2239, Combining Attributes 3601, Before apply boxcox 16953, Sex 33355, Timeseries decomposition plots weekly sales 12369, Checking and removal of null values 30882, Set predictions 1 to the model s predictions for the validation data 40016, Age gender and cancer 841, Linear Regression 4411, Missing Values 7867, If you create a model saying that only woman survive it would have a score of so the mission is to create a model at least better 13417, LightGBM Parameters tuning 8012, Ensemble Methods 21762, Missing values in indrel 1 99 8494, We can also visualize the partial dependence of two features at once using 2D Partial plots 2346, XGBoost Native Package Implementation 13350, Import Libraries 21520, Lets visualize the Loss over the Epochs 21940, Spark 16658, Fare 26913, Predict test data 22692, Main part load train pred and blend 18908, Fare Feature 34760, Model Creation 42126, Model parameters 34470, Save the data for training 9596, Unique values of a Feature 5968, Calculated fare feature 34905, Simple EDA 2906, Handling Missing Data 9037, Kurtosis is used to describe the extreme values in one tail versus the other 7951, Tuning on Adam optimizer params 1020, print our optimal hyperparameters set 7317, We could know that the numbers of survived and died people are close to balanced 41464, Plot the histogram for Embarked Val 12740, Some common python libraries for data analysis and plotting are imported you already can tell how straightforward the model is by the number of libraries imported 42177, So we use the one hot encoding procedure 34794, The residual distribution is normal 29459, Text embeddings and Setting the initial weights for our NN 6809, SVMs aim at solving classification problems by finding good decision boundaries between two sets of points belonging to two different categories To understand how it works you can refer to this webpage tutorial com 2014 11 svm understanding math part 1 1612, Label Encoding Non Numerical Features 26016, After a good amount of homework I came to a conclusion to impute the values rather than dropping the columns 286, I wonder if one sex was more likely to survive than the other I expect that women and children were evacuated first 14450, Normalize Data 13172, let s take a look into a summary of our numerical variables 32661, Replacement strategy p 25404, The Softmax function allow us predict the model because it normalize the data in one hot encoding 36243, After extracting all features it is required to convert category features to numerics features a format suitable to feed into our Machine Learning models 1628, Checking for NAs 16012, Pclass 10910, Tune the Decision Tree Model with Feature Selection 42464, ps car 12 and ps car 14 3069, Change Strings to Numbers 28580, 1stFlrSF 11050, pipeline xgb 12534, Calculating score of each model 21080, Missing value is data set 5497, For implementing Grid Search in Random Forest Regressor model I have refered this 30083, Logistic Regression Algorithm 15148, Concatenate both Train and Test datasets for data cleaning 20131, In order to make a good logistic regression model we need to choose a value for regularization constant C 24384, We have two outliers where SalePrice is 700000 17531, Some titles are to rare 12490, Lets use lgbm to predict prices 20292, SibSb 26503, The first layer is a convolution followed by max pooling 12345, Basement 6391, Statistical Description 26248, Sample Predictions 11921, Best Model 38691, After Mean Encoding 4518, Ridge Regression 31100, Handling Missing Values in Categorical data 5900, u Using Heatmap u 36532, I guess we should be aware of the dataset but given my experiment here 21477, Training Data 38586, Checking Accuracy 621, Cabin known 38219, For each image in the test set you must submit a probability that image is a dog 37340, Combine predict 38204, Fit RandomForestRegressor 11094, Modeling 32116, How to normalize an array so the values range exactly between 0 and 1 28475, New feature Weekend weekday transaction 5511, Distribution with respect Survived 5354, Diplay combined 3D subplots 33801, Pairs Plot 20647, We are now ready to define our neural network model 37036, Does the lack of a description give an information on the price of an item 14167, I just repeat the same process for test data 8993, There are 3 rows where this is the case so I am going to assume the data collector accidentally forgot to calculate the MasVnrArea 15375, We use the 31519, Using the roc auc scoring parameter as that is the metrics to be evaluated in the problem statement 27073, do the same for test set 34686, Clipping target variable according to the competition rules 23541, Labels are 10 digits numbers from 0 to 9 21183, Check how many parcel values occur 1x 2x 3x times 36864, Using GridSearchCV with KNN takes very long for this dataset 5401, Keep Cabin and impute Cabin or not 28456, Out of the remaining three columns transactiondate is a datetime object 16495, Logistic Regression 33049, I can try with mutilple layers as with the neuralnet 22341, Tokenization 5501, Using the titles obtained we ll fill the age 4224, Label encoding 10731, Lets try to make a new feature 20708, Second column 5607, Skewness Kurtosis 29178, Normal Log Transformation 10854, Checking for the number and percentage of missing values 18960, Display distribution of a continuous variable 25376, Complie the model with loss function and optimizer 17003, Explore Target Variable 39429, Predict for df test 29565, ID 4543, decision tree 16056, Age vs Survived 1031, Data cleaning 23642, Define Dataset 3794, Quantitative and Qualitative 21350, Preprocess a single image 41241, do predictions on our training set and build a submission for the competition 23512, There are elements in the class The classes are revealed by text cleaning in the model The strings in the class before the text cleaning differ not only by URLs but by via usatoday or via USATODAY at the end of the strings Mean target is in the class relabelling to 22636, Model 1 Naive Model 35232, This is essential to know before we perform modelling 12336, FireplaceQu 20565, Make Predictions 31086, LotFrontage font 29008, Creating combinations and running models on them 27357, Feature Engineering 31542, Alley 2220, We visualize how the housing market in Ames IOWA performed during the years and how bad it was hit by the economic recession during the years of 7525, Depending on the categorical variable missing value can means None or Not Available 38946, Yearly plot 28931, DAE Generator 31905, Train the model 26893, Create Submission File for approach 6 40685, NOW WE CAN EXPLORE OUR FEATURES FIRST LETS EXPLORE THE DISTRIBUTION OF VARIOUS DISCRETE FEATURES LIKE weather season etc 12956, Filling missing values in Embarked variable 24279, Model cross validation with K Fold 38264, We apply data cleaning steps to both our training and test data 16780, Random Forest Classifier 18463, Apply the model to predict values 37638, Just to make sure that our predictions make sense we display 10 different images from the test set for each of the 10 classes 703, Recall the form of a standard linear regression 42009, Sorting with conditions and if there are any that matches 7047, Type of roof 32350, Train Test split 29396, we finally reached the last part of the notebook where we submit our predictions of the test data Just as we pre processed our training data we preprocess the test data make our predictions with our CNN model and submit our predictions 11169, Since area related features are very important to determine house prices we add one more feature which is the total area of basement first and second floor areas of each house 4236, Once trained our model we can then visualize how changing some of its Hyperparameters can affect the overall model accuracy 2479, Using a model found by grid searching 19881, Quantile Transformer Scaler 15823, Standard scalar for training data 11721, Radius Neighbors Classifier 501, Pclass Feature 9845, submit our solutions 15368, Handling Age Feature 36785, Word Tagging and Models 2029, Dealing with NaN Values Imputation 19138, Model 1 with GradientDescentOptimizer 6135, This house is almost new 6739, LotArea Vs SalePrice 12806, Data Analysis 40740, Simple CNN 16906, New Feature Connected Survival 21597, Webscraping using read html 32560, Anatom 27547, Display world map with different porjections 5170, EDA based on the my kernel FE EDA with Pandas Profiling eda with pandas profiling 1858, ElasticNet 40439, Image Augmentation 5839, These are the top 5 recorded values under SalePrice column and it is int type variable 22900, Mumbai and Nigeria are still on the top 3423, Some of these could be duplicate groups 40467, Sale Info 9469, Unique values 16888, FamilyType 10834, We start from testing number of classifiers to choose the best one for further fitting 7879, Based on the exploration of the data I propose to discretize the Fare in four states 20206, impute mean value to numerical features 18220, Ensemble 15 CNN predictions and submit 8733, Bivariate Analysis 16149, Binning 3316, Combined model I also tried stacking which can improve the score but the effect is not ideal 11125, Model Stacking 30572, In the code below we sort the correlations by the magnitude using the sorted Python function 23544, Define the model 8491, Create Submission File 20273, Analysis of categorical features with levels between 5 10 19863, In this method there is no fixed approach for setting the upper and lower threshold and based on purely the problem statement and the dataset we have 43100, In embedding layer trainable is set to False so as to not train the word embeddings during the back propogation 36492, Dispatch all samples in fold X in 5 folds 33443, LogisticRegression 39242, Feature engineering 14480, kids on 1st class was one found and died even 1st class children weren t there 6153, Set a list of basic regressors 29743, Rank averaging 6319, Random Forest 949, Pearson Correlation Heatmap 25439, Initializing Optimizer 8093, DATA EXPLORATION 11461, SibSp and Parch 2800, check train test set Checks the distribution of train and test for uniqueness in order to determine the best feature engineering strategy 36280, Sex is categorical data so we can replace male to 0 and femail to 1 23689, Check image size 24903, EDA 5907, Before applying SVR Scaling needs to be done 26841, Preprocess for Neural Net 19939, Categorical values 27486, To easily get to the top half of the leaderboard just follow these steps go to the Kernel s output and submit submission 2028, Loading and Viewing Data Set 34390, Locations 39165, How to use a predefined architecture from fast ai 6605, Random Forest 10111, Exploratory Data Analysis EDA 17440, Fare 9299, We can use a pandas function called get dummies to as the function says get our dummies 39997, Fixing Outliers 7903, I keep two of these models with 6 neighbors and with 10 neighbors 27087, The transformations are standard Python objects typically initialized by means of a training corpus 20311, check out what are the top 10 goods bought by people of each cluster 6493, Maybe a good idea would be to create some new features but I decided to do without it 41478, Plot a histogram of AgeFill segmented by Survived 20646, We can then use the maximum length as a parameter to a function to integer encode and pad the sequences 40617, to check if Linear Regression finds a similar solution 3256, Estimates of Locations where STATISTICS comes in place 9296, Regression on survive on PClass using a Categorical Variable span 32809, Level 2 Random Forest 26084, assign the values provided by the model 38829, Define binary cross entropy and accuracy 34269, LSTM model 20607, Parch 33069, Modelling 28764, we shuffle the data 20488, Go to top font 36814, Machine Learning 911, Imputing Missing Variables 19771, Dimensions 5902, Train test split 22067, Jaccard Scores for using TEXT as SELECTED TEXT in TRAIN data 800, If we split by Title 1 we ll have the two following nodes 26400, In general the pobability of a woman to survive is more than three time higher than for a man 4742, now we can do some plotting with those null value 25164, As we have very few points with NULL value then its better to remove them 12880, Passenger Class 4819, Remove 43346, We use Cross Entropy ti calculate Loss optimizer adam and metrics as accuracy 16103, We now convert the categorical value of Embarked into numeric 22041, One more variable from floor area could be the difference between full area and living area 19460, Compiling the model 20379, We all know that id column isn t important to us so we drop it 39724, I m still pretty new to python so I m not sure what the cannonical way of doing this is but using 36886, dense 1 10568, We use Alternating least squares ALS algorithm in Pyspark Ml library for recommendation To read more you can visit at collaborative filtering html 28809, Middle out 36570, Train model 25418, Alright let s now explore this newly created column 218, Model and Accuracy 3820, Notice that as our confidence interval becomes bigger the interval of possible population means becomes bigger 21479, We need to build the augmented test that we use to cross validate the test predictions 21133, It s good idea to investigate first variables which have Qual in names cause this shortcut refers to Quality in other words we expect that some levels indicate lower quality while other ones higher one what can enable us to order them 38247, Most of the test data is from Asia and Row spacing is also comparitive to Europe 23833, Duplicate features 31379, Rotate Image 3513, The variable importances from the boosted model on the reduced dataset 27855, Drop columns with more missing values in Test Set 28276, Each image in the training set is 7562, Imports 14829, Drop Passenger ID and Cabin 21913, Normalizing features 25005, Test Data 14481, Embarked S survived more in female in every passenger class in male also same the same 3804, Ridge 26623, we plot the image selection 15486, Scale Continuous Variables 22648, Initializing 37778, Loading the data 28301, Choose optimal threshold by val set 5299, I now build the five base models 7195, Clearly we have not a normal distribution in our target let s solve this with log transformation 6848, Analysis Name column 22687, Get correct labels from test data with labels 17405, Out of Fold Predictions 39856, Augmentations with ImageGenerators 21558, Sex 33748, The following be very slow 9689, How to decide out of two which feature we should remove 11525, RF Regressor 34511, Replace Outliers 32715, Attention Model 40824, Machine learning 32718, BERT Transformer 28509, Here is some sample rows 12280, Generate output the mean median max and min of the scores fluctuation 21157, Comparing used methods 13562, Names Column 14197, take a look into the data 6947, Silhouette plot 40277, As any model we build we most certainly be performing feature scaling and one hot encoding it is good idea to use the same basic transformations on our baseline model 19633, 1 n classes benchmark 42174, Data normalization in Keras 8004, Train SVM Regression 31515, Unsatisfied customers 42567, Training 2426, Lasso Model 27070, Intersection 7232, Dividing the Dataset 942, Split Training and Testing Data 36742, Calculate the Mean Absolute Error in Validation Data 1371, let s categorize them 17922, Successfully completed one hot encoding on the dataframe s columns 24660, Loss function 28208, Modeling 25689, How Kerala Flattened Curve 40767, From this point forward 19100, Plotting the Accuracy of the models 38091, In order to avoid overfitting problem we need to expand artificially our handwritten digit dataset 33577, Transformation 628, The factorplot suggests that bad tickets are worse for male passengers and 3rd class passengers 42429, Lets Do A Simple Submission 19307, Evaluation prediction and analysis 2994, There you go SalePrice looking off the charts 40729, Predict on Testset 29806, Displaying Glove WordVector of a word 15502, SibSp and Parch 18298, Looks pretty good we now generate some texts 19292, Creating keras callback for QWK 36869, NN Classifiers with Keras 6012, Hasilnya masih belum memuaskan ya bagaimana kalau kita pakai target yang original yuk kita coba 822, Relation to SalePrice for all categorical features 6571, Fare 37447, In this section I am trying to build a multi input model using BERT embedding for predicting the target keyphrases 34487, Write the Softmax class 2531, Confusion Matrix for the Best Model 118, take a look at the histogram of the age column 8135, I took several classification algorithm and I compared their RMSE on the train data in cross validation 824, Correlation matrix 1 20916, Lags 27475, Modelling 33997, casual 5543, Tune Model 33639, Target Variable Distribution Univariate 9609, People were addresses as Mr Mrs Miss Master Rev Dr Major Mlle and Col on the titanic 17631, Assigning Miss age smaller than 14 to Girl Title 19461, The loss function we ll use here is called categorical cross entropy and is a loss function well suited to comparing two probability distributions 13527, Download datasets 38218, Predict 38286, Imputing data in Remaining Columns 28635, GarageArea 26946, AMT REQ CREDIT BUREAU QRT 18039, Predictions 1905, Correlation in Data 39888, Basic 17185 22417, based on that and the definitions of each variable I fill the empty strings either with the most common value or create an unknown category based on what I think makes more sense 7795, investigate how well this model did 1295, Feature Importance 6 21216, In deep learning always remember we can have large datasets most of the times so we don t split in 75 and 25 ratio rather we give a small percent like 4 10 for validating and testing and those are more than enough 16306, Sex vs Survival 7876, This continus feature could be converted in a continues feature in order to increase prediction of the model 5674, Pclass 21122, we investigate the quality of the given data 5361, Diplay many types of plots in a single chart with table in it 15204, Get title from Name and average survivability per title 39424, get rid of Ticket for now 6611, we have to deal with Categorical Data types 27133, Visualize Discrete Features with their average Sale Price 23060, We checked the length the head of datasets all good we can start building our model 5675, Create a new Pclass Fare Category variable from Pclass and Fare features 223, Model and Accuracy 33781, Generating CSV File 27326, TTA Analysis 16828, Creating new column predicted with if Survived Prob else 9622, Evaluation 22706, Train the network 1851, Which Numeric Features are Candidates to be Transformed 9194, The roundabout 300 filled examples for the Cabin information are fairly one sided distributed to the first class 31530, Normality Test 40181, Code from notebook 13385, Categorize passengers as male female or child 32050, Let s check whether all the missing values have been filled 17992, We first group the passengers as per their titles and then fill the missing values for the age using the median age for each group 43352, But for each image we get probabilites for each label 34341, Transform Target and Redefine Features 13353, View profile report of training set 42148, Define VAE Loss 31949, LSTM models 28209, Normalize the input features using the sklearn StandardScaler 11417, Use Case 9 African Adult Literacy Rate Butterfly chart 9795, Here there is a left skewed data 39744, Embarked 11685, Passenger s Cabins When you loaded in the data and inspected it you saw that there are several NaNs or missing values in the Cabin column It is reasonable to presume that those NaNs didn t have a cabin which could tell you something about Survival So let s now create a new column Has Cabin that encodes this information and tells you whether passengers had a cabin or not 37448, Below function is from this by xhlulu this is used to encode the sentences easily and quickly using distilbert tokenizer 15922, Model Selection and model tuning 7644, remove highly correlated features 20740, Functional column 20675, Deep Learning Model Life Cycle 38656, Survival Rate 10349, we split the data to training and testing datasets 24256, Titles 7017, Evaluates the present condition of the material on the exterior 29006, NN 3632, Pclass Sex SibSp Parch 11237, The Grid search Best params are 9276, Showing one scenario of how I tuned the parameters of my model 6491, Data preparation 31709, Submission 31099, CATEGORICAL DATA 28153, This knowledge graph is giving us some extraordinary information 38417, For this classifier we use a special submission algorithm 36304, Submitting the solutions 36784, Regular Expressions 20955, Train the model with fit method 29195, Residual Histogram to visualize error distribution 22119, How to interpret Feature Importances 12771, Fix Data using 3 various methods 6140, Feature selection 8365, Integrate into test the title feature 9290, Imput Missing or Zero values to the Age variable span 20232, Sex 29918, we define the hyperparameter grid that was used 26921, Test for homoscedasticity is the dispersion of scatter plot 15740, we can creat a new feature about family size because SibSp and Parch feature are about same meaning so if we merge them we can creat family size feature 33511, Spain 2111, New features and what suggested them 5023, Standardize 14790, Family size features 33276, Categorical Features 39237, Remove duplicate shops 23891, There is an interesting 2 7996, Create Pipeline 29129, Model1 Error 5016, Missing Data 22979, Median of sales per month different from month to month 877, pandas get dummies for categorical features 23218, If you want to read more about this Baian library read 21400, Training the model 3252, Bar Plots House Style Frequency 19123, ELI5 30319, Drop train data with no candidates of start end positions due to poor segmentation of selected texts 24417, Build Year 31414, It turns out that you dont need to override add test to work 17625, Cabin Cleaning Assigning the missing Cabin values with repect to the Ticket number 41318, Use the next code cell to preprocess your test data Make sure that you use a method that agrees with how you preprocessed the training and validation data and set the preprocessed test features to final X test 22857, Russian Stock Exchange Trading Volume in Trillions 19560, basic utils 16347, Manipulate data 32902, Convert an image as numpy array 4956, Gradient Boosting Regression 4243, Artificial Neural Networks ANNs Tuning 36735, Decision Tree Model with max leaf nodes specified 41012, It looks like 9 of those 11 passengers are in the test data so there is definitely the possibility to find other females who died nice 35356, I am using Sequential model and used Flatten layer to convert tensors into arrays Using relu activation REctified Linear Units with different input image parameters I degraded features vectors to Lastly I used Softmax function with output entries to I compiled model with adam optimzer and used loss function as sparse categorical crossentropy At the end I trained model using data with epochs Epoch is training loop forward and backward to train model 19333, Model Performance Analysis 33850, Univariate analysis of feature word share 33715, FEATURE SibSp 30697, Dividindo a Base 23423, Average word length in a tweet 39180, apply the postprocessing to build the noise 23953, Building the model 12350, BsmtFinType2 Rating of basement finished area if multiple types 7816, Feature selection 27648, Predict 38646, Fare 2081, You can check yourself the public LB score of this model by submitting the file submission3 26439, let s build a ANN classifier and train it 41671, take a look at some benign tumours from the train set 2287, Precision Recall Curve 13412, True Positive Rate 42704, do similar analysis for int features 5945, There are many missing values in test dataset 17845, Go to top font 1524, Numerical Features Age Continuous Fare Continuous SibSp Discrete Parch Discrete 31847, And then drop types 16896, Title Definition 1241, Blend model prediction 19663, Correlations 4719, Following is our new cleaned dataset on which we be applying our machine learning models 17369, Observations 5657, Read the Train and Test Data 15958, Correlation between the Numeric Features 1723, Embarked Count 34625, We load the training set 27035, Unique IDs 38942, We have now identified the outliers in the data 12346, BsmtQual Evaluates the height of the basement 32793, Sklearn K fold OOF function 18191, a quick look at the distributions 29714, List of columns with too many outliers 35200, Lasso Regression L1 Penalty 11676, It looks like fare is correlated with survival aboard the Titanic 10918, Submission 28341, Analysis based on FLAG OWN REALTY 27534, Display the confidence interval of data and used on graphs to indicate the error 5417, This is pretty straightforward 19573, handle item cnt day outliers 2525, Bagging 42411, Training time 13832, Age 12793, We plot the accuracy stories we have retrieved 27095, Reaching the Top Ten 7220, Electrical Null Value 3339, Notice the difference of Medians for train and test data 32074, n components decide the number of components in the transformed data 300, don t really know what to do with these 40276, Before we can begain with feature engineering or model development it is important to set our baseline across which any future improvements be measured 14565, lets check the missing values again font 1639, We use a lot of features and have many outliers 10248, Go to Contents Menu 24473, Normalization Each value in the dataset is a greyscale value between and It s best to normalise the data so that each value is between and before applying any models 27844, Plot confusion matrix 26748, Distribution of median prices of items over the years 1942, Street 8991, and now I can drop MSSubClass 25682, System When there is NO SOCIAL DISTANCING 33664, Current Date Time font 42348, tune the parameters of model My role of thumb is finding the balance point between number of rounds and the learning rate 1711, Imputation using bfill 2328, Predictions and scoring regressions continuous variables 11155, GarageYrBlt GarageArea and GarageCars Replacing missing data with 0 Since No garage no cars in such garage 33325, Model Training 36664, Reading a text based dataset into pandas 19342, The category name is a categorical variable so we have to turn it into dummy variables and check the correlation between them and the price 4404, Classification Report 10152, Scatter Plots 1404, Missing values in Embarked and Fare variables are very easy to imput because we can use the most popular value or something like that 30333, For convenience we create a dictionary that takes as keys the animal classes 17546, Fill the missing Embarked Value with the most frequent one e S 13906, Classification report 20302, Dropping some featutes 24504, Submission 29739, We have got a better validation score in the probe As previously I ran LGB BO only for 10 runs 23666, First we need to create miniature training and validation sets to train and validate our models 2722, In order to further improve our model accuracy 35849, we reshape it into proper image shapes as we be using convolutional networks 4066, we are going to assign to each row with missing ages the mean value of the age for its respective cluster using the DBSCAN algorithm 31474, Dataset 24547, Again we exclude the dominant product 39132, The data is separated again in two variables train and test 14451, go to top of section corr 2472, Making FARE BINS 38287, focus on following set of columns first 8472, Compressing Data via Dimensionality Reduction 27027, Calculate probability use hypothetical test 27198, This image gives us the correlation analysis We use it to explain the relationship between variables and we can say that the relationship increases as the ratio on boxes approaches 4800, Outlier Handling 4906, This is the Normalised Data 28690, SaleCondition 21078, Correlation plot 10842, Check values in each column 16580, More Feature Creation 30695, Uma vez com os dados ordenados pegaremos o id doprimeiro da lista ou seja o que teve o maior ganho 2729, RandomizedSearchCV 19385, score 1860, Non Linear 36474, Tweet authors 35482, Intialize the Value 12364, Functional Home functionality 21768, Assigning missing incomes by province is a good idea 10548, Ridge performs better than the Lasso on this dataset 31317, Encoding 28525, BsmtFinSF1 11286, Fill missing values with the most common values using the groupby function 15928, Parch 31597, import the necessary packages 32419, Another Example Increasing Padding and Stride 19716, visualize some of the predictions the model made 10578, Logistic regression 5293, I ve tried various transformations and found that log transform somehow works better for me 3854, Feature FamilySize 13995, Impute Title with it s mode 41960, In simpler terms it is the process of converting a word to its base form 19586, item 11413, Use Case 5 US Wages Tableau Visualisation 7607, ColumnTransformer 18060, Splitting the Data 12377, Removing of outliers in Sale Price 15205, Make features related to family size 26103, Linear Regression 19979, MLP ReLU ADAM 18464, Libraries to import 1192, we find that these are outliers 34465, Rolling Window Statistics 23313, Quick Baseline with XGBoost 17969, Experimental NA scalar to denote missing values na scalar to denote missing values 9825, SibSp Number of Siblings Spouses Aboard 15514, Embarked 34085, Simple Logistic Regression 36824, we use the fit transform method to transform our corpus data into feature vectors Since the input needed is a list of strings we concatenate all of our training and test data 34971, For Loops to Test the Various Parameters 15707, Most fare tickets are between 43013, Logistic Regression 4915, Numbers our not my Stuff See some Graphs 34733, Resampling the training data 36918, Missing Values 10745, From the scatter plot there are two outliers with gross living area 5642 4676 but the SalePrice is low 17635, Age Cleaning Fill missing Age values by taking the median Age of its corresponding Title Pclass 33236, Convert the features from dictionary format to pandas dataframe 24422, Cultural Recreational Characteristics 4118, Ordinal data mixes numerical and categorical data The data fall into categories but the numbers placed on the categories have meaning For example rating a restaurant on a scale from 0 lowest to 4 highest stars gives ordinal data Ordinal data are often treated as categorical where the groups are ordered when graphs and charts are made However unlike categorical data the numbers do have mathematical meaning For example if you survey 100 people and ask them to rate a restaurant on a scale from 0 to 4 taking the average of the 100 responses have meaning This would not be the case with categorical data 11305, Import Python Libraries 30685, Transitions out of State 0 20204, Create function to set mean value to numerical features and mode value to categorical features 591, First a broad overview 12016, Gradient boosting is an ensembling model based on boosting method 29454, Here we ll use counter vectorizer to specify one column for each word in the tweets and use it as a feature for prediction 24165, BBoxes with Masked Sample 5356, Diplay relationship between 3 variables in bubble 8801, First impute the missing values in Bsmt Features 11758, Both our Lasso and Elastic Net models look significantly better And we have the parameters we need now to train them on our full data and use them for predictions 16515, MLP Classifier Multi layer Perceptron 23450, Hour 33670, Month font 26707, Plotting sales ditribution for each state across categories 14735, True Positive Rate 33610, I am going to compile my model with adam optimizer as i think it is working better than RMSprop for me 34948, Predictions 18438, Interpretation font div 3545, We are gonna implement a function that be useful to visualize the survival relatively to a certain feature 129, False Positive Rate How often the model predicts yes survived when it s actually no not survived 2229, Interesting insights 7886, The same analysis for the IsAlone feature 5848, we are ready with our most influencing features lets check again how the are related to SalePrice visually 40920, Fit 761, Train the model 27181, XGBoost Regressor 13837, Which features are numerical 37870, One Hot Encoding Categorical Variables 12666, The metrics were calculated as described below 36575, also have a look at the other end of the spectrum 21470, For each word of a sentence that isn t a stopword we randomly choose between itself and all his synonyms in order to replace it in the modified sentence 42366, Male and Female are multicollinear columns 5917, ANN 29020, SibSp 2727, Default Model 34412, Download data 37990, Here we use GlobalAveragePooling before FC layer 38305, lets go to model testing 9946, removing the useless columns 21102, Check the distribution for detectiting outliers 6013, Kita gunakan automl yang ada di jcopml gunakan AutoRegressor untuk kasus regres 1511, lets plot them all 26674, Annuity Amount 23686, Check number of files per folder 23688, Barplot 20134, Predict on test data 30329, This is a function to reshape the image 14605, KNN Classifier 5008, Numerical Features 5411, This as well 37758, Generator Approach 10377, Prediciton and selecting the Algorithm 24250, Fare 41016, The unique values count confirms that generally the assumption that these groups lived or perished together is absolutely correct that is the real power of the WCG model 31324, Customized Metric 41540, Linear Discriminant analysis 7210, Univariate analysis of Target variable SalePrice 42999, Make Submission 21264, Data augmentation step 42416, Simple Feature Engineering From Timestamp 31401, We can plot the loss and val loss acc and val acc 22448, Ordered Bar Chart 12248, Loading the data 32398, Ensembling With Meta 13764, Validate and Implement Model 26749, use a threshold of 3 30758, For given problem we must define desired RMSE and it should be not more than 10000 36215, Analyzing categorical attributes 18133, Model Performance Review 16532, Visualizing given dataset 34710, Mean over all shops 26746, Different states have different SNAP dates so we have to view them separetly 7428, Random Forest 4056, The transformer dummify variable be used to handle the categorical columns 42023, Extracting the first alphabet of a word ending with a stop 1153, Predictions from our Model 43165, Cropping and Resize 10460, We might also be interested in strong negative correlations it would be better to sort the correlations by the absolute value 1005, Fantastic But wait a minute let s investigate a bit more 23876, Transaction Date 1337, Converting a categorical feature 21853, Training on ALL IMAGES 24180, Starting point 27072, I want to check if there is some duplicated tweets it could be a retweet If we know that a tweet is fake or not so the other duplicated tweets get the same class 9698, go through different categorical columns 41651, Predict on test set 14379, Females from 18 to 30 were most likely to survive 19671, The most important feature created by featuretools was MAX 6183, The number of parents and children present 11128, Edit Look for fliers in other columns 26083, We need to reshape data 14986, Feature Scaling Normalization 24510, Train model on imbalanced data 15571, A family size feature 36518, Embarked 18002, Processing the training and test set together 3924, Random Forest Classifier 38934, XG Boost 7890, These new features provide a more clear distribution that the dataframe without features 12317, I think that outlier 10121, Look at that if we had dropped cabin we d have lost so many information 6637, Above we are creating boolean values for the model to understand 28776, draw a Funnel Chart for better visualization 1592, In order to be more accurate Sex feature is used as the second level of groupby while filling the missing Age values 34651, Item categories data 10693, Fare processing 15602, Pairplots 31720, Train AGE Variable 8738, Apply Log Transform 35627, Experment 3 32576, We need to set both the boosting type and subsample as top level keys in the parameter dictionary 20557, Model training 1133, Feature selection 1013, All good apart from Survived it is still a float 32898, Validation dataset 15339, LOGISTIC REGRESSION 17624, Ticket cnt 10663, Model Evaluation 14587, It is very less value we replace the missing value with the most embarked port e S 14340, Try a RF model 952, Out of Fold Predictions 39787, taxvaluedollarcnt vs nearest neighbors taxvaluedollarcnt 32161, And the last note here 43332, check some of our prediction 24448, Train Test Split 42756, This list be passed into the network It is composed of 14 numpy arrays containing our categorical features that are going throught the Embedding Layers 13 layers The last element of the list is a numpy array composed of the 173 numerics features added to the 3 categorical features that have at most 2 distinct outcomes 23243, Building Your Model 9400, Hyperparameter search with model combination 29600, We can now create a new optimizer with our found learning rate 4779, Voting 23760, Adding it to final dataset 10354, Testing Different Models 13591, Running LogReg HyperOpt 10940, Check the summary of train data 7649, convert numerical variables to categorical variables 34417, First we analyze tweets with class 0 41670, Final predictions and submission 16641, Naive Bayes 21211, check any missing values are there 12948, Declare feature vector and target label 38734, First let s deal with the missing Age values 34297, This is the activation of the first convolution layer for the benign image 28805, things get a little hazy Its not very clear straight forward 1315, Feature Transformation 25731, build Model 26335, TF IDF Vectorizer 28772, We have one null Value in the train as the test field for value is NAN we just remove it 12360, MasVnrArea Masonry veneer area in square feet 41374, Feature Engineering 33578, Meter 27394, A side note that i tried random forest and decision tree but the RAM was crashing so i removed them 14298, again Check for missing values 33340, Joining df 37342, VGG 19 15963, Name Feature 20401, visualize the embeddings by projecting embeddings in 2 dimensional space using singular value decomposition 36079, Seed 24840, LaNet5 13042, Creating Family Size variable using SibSp Parch 19050, lets set some seaborn parameters 20914, Predict test data 38973, As we added three new rows in train X list we need to add three new rows in train y list 30646, Random Forest Classifier 30977, Below is the code we need to run before the search 33847, Checking for missing values 2396, Using missing values as a feature SimpleImputer add indicator True 32388, Adversarial Validation 452, Lets apply Modelling 15403, There are multiple age values missing in both training and test data 9699, After much exploration I found that In some columns one category is highly dominating 1970, Gaussian Naive Bayes 37828, Model Building 17038, Feature Correlation and Dependencies 29598, we can plot the learning rate against the loss 19619, Outliers smoothening 18966, Display the density of two continuous variable with custom bin size 37312, Rfe Selected columns 2821, keep only the column that have a acceptable information gain 20590, SVC 1766, We do a simple bar plot to check title vs survival rate 36301, Below we set the hyperparameter grid of values with 4 lists of values 25450, Applying Decision Tree 14060, SibSp vs survived 30088, Random Forest Algorithm 37402, Identify Correlated Variables 5918, DF for ANN 12465, Submission 1066, We stack all the previous models including the votingregressor with XGBoost as the meta regressor 19419, and the last token is Alright time to write the processing function sup processing sup 8436, Fireplace Quality Miss Values Treatment 3958, Label Encoding 31511, Plotting the top 30 features 31902, We split our training data into train and validate datasets in order to train our model and validate it using the validation data set to avoid overfitting before testing the model on the test datasets which is as real world data for our model 33817, As expected the most important features are those dealing with EXT SOURCE and DAYS BIRTH 3509, Classification report 25431, Test 13499, Is Baby 3925, Gradient Boosting Classifier 159, EDA of test set 31279, Below are the sales from three sample data points 25758, Making sure the transformation works on the original train images and on test 40102, Clean up the memory 4203, In this step we try to feed a Lasso regression model with all of our variables 19942, Since we have two missing values i decided to fill them with the most fequent value of Embarked 14169, After adding all these new features we need to check whether we have null values and deal with them 23930, Output 11553, Influence of Categorical Features on SalePrice 13826, Calculating F1 Score 2 precision recall precision recall 34745, INTEGER ENCODING ALL THE DOCUMENTS 5661, Fill basic missing values for Embarked feature and convert it in to dummy variable 32963, the categorical features 20464, Type of the housing of client 16194, And the test dataset 19888, There is more than one way of determining the lag at which the correlation is significant 32807, what just happened there Our CV score for each training fold is pretty descent but our overall training CV score just fell through the crack Well it turns out since we are using AUC Gini as metric which is ranking dependent and it turns out that if you apply xgb and lgb at level 2 stacking the ranking get messed up when each fold s prediction scores are put together 19727, For week days vs week ends 30653, Is that real 222 unique value without any duplicates 2648, There are just 2 NaN values in Embarked column 19915, Test Prediction 437, MasVnrArea and MasVnrType NA most likely means no masonry veneer for these houses We can fill 0 for the area and None for the type 14501, Missing Values 4931, Another good practice when doing DS projects is to look for all sorts of correlations between all features 2131, the bedroom proportion is never helping 23632, get the test set predictions as well and save them 3967, ElasticNet 28335, Analysis based on OCCUPATION TYPE 12634, Sex 8096, Age vs Survival 25916, Creating Submisssion File 38044, Sample Mean and population Mean 33845, Checking for duplicates 29801, Fetch list of word vocabulary 14617, We are summing over all n passengers and our initial predictions y are relatively close to Thus the term y n t n is always close to for t n or close to for t n There is nothing wrong with that BUT you are multiplying with x n fare If it s an outlier this yields a high contribution to the gradient in the sum Even if all other contributions might be of a low value one high outlier value already shifts the entire gradient towards higher values as well This is a bad learning behaviour If our model gradients are mainly driven by outliers it tries to learn the survival of these exotic values ignoring the majority of all remaining passengers Uff 43099, its time to build the model 6921, Fit Model 1186, take a closer look at some of these lower numbered variables 7621, Loop over GridSearchCV Pipelines Linear 15302, Which is the best model for prediction 28351, Analysis based Averages values 1799, How about one more 1997, It s a nice overview but oh man is that a lot of data to look at 14811, SipSp Survived 32861, Creating the label 3510, This model appears to be the best preforming in terms of precision and recall on the training set 36285, Better let s convert to numeric 24477, Create train and validation sets 34343, Setup 14507, Fare 38452, process all questions in qid dict using SpaCy 37728, PCA Analysis 31276, Rolling Average Price vs Time TX 40940, We are calculating the feature importance because the variables are just too much so we only need concern ourselves with the ones that are useful for our analysis 4171, Equal width discretisation with pandas cut function 4708, i wanted to combine the linear models to tree based models 36602, Printing the predicted frequencies of train data 36934, After filling the missing values in Age Embarked Fare and Deck features there is no missing value left in both training and test set 15814, Female passengers survived more than the male passengers 35453, Data EDA 36425, Categorical Features 11407, Model Train With Random Forest 43379, Before we start with building targeted and non targeted attacks let s have a look at the first digits of the test set 40923, Displaying Samples 12870, Feature scaling 42267, There is no difference in this boxplot 14788, Age 26724, Plotting daily sales time series 25951, We able to grab ranking position within top 10 on test data just by using EDA and FE for numerical features and with simplistic model In the next section we work on categorical features as well as model selection strategy We store all the original and newly genearated features for furhter step 34411, Import libraries 5452, Setup a small subset of the data 24923, Texts 24578, This train model function comes from training and validation code 6066, WoodDeckSF mostly 0 maybe binary is better 24251, Observations 42078, demonstrate the half bathrooms unininterest from a statistical point of view 19284, If the model is on the GPU we have to transfer the data to the GPU to run it through the model 20639, Trigrams 17031, Fare and Age mean arde highly skewed 24249, Family SibSp and Parch 36032, Holdout evaluation Logging for error analysis 10802, Before we start the prediction we need to imput two empty values 36799, we define the tag pattern of an NP chunk A tag pattern is a sequence of part of speech tags delimited using angle brackets e g DT JJ NN This is how the parse tree for a given sentence is acquired 16441, A basic trend can be found from the graph 22069, Model 37713, Start training 20359, This corrected solution improves the final leaderboard score to places improved as of the publication of this kernel The error rate is improved by from the human in the loop step 526, Highest survival rate for women in Pclass 1 or 2 43118, apply OH on X test 14860, All we have to do now is convert them into the submission file 22042, Age of building might have an impact in the rental price and so we can add that one as well 31185, Evaluating a classification model font 24055, Each sample s missing values are imputed using the mean value from n neighbors nearest neighbors found in the training set 26545, Epochs and Batch Size 20944, MNIST dataset 10517, try to tune Parameters 18061, TF IDF Vectoriser 22478, Calendar heat map 38661, Count of Siblings 11847, A spy in disguise 26910, Get the best parameters 40801, Model tunning 13413, False Positive Rate 29748, Class distribution 35604, Confusion matrix 5437, MiscFeature 9283, Univariate Analysis 24976, Save prediction 25277, Read the train and test datasets 19609, Following variables are highly correlated 31696, Feature Engineering 4272, MasVnrType and MasVnrArea 3530, Count Plot Neighborhood 12905, Title is quite important primarly because it indicated whether a passenger was male or female 9382, interesting one missing value found in test data 605, For a view into Pclass vs Sex let s use a mosaic plot for a 2 dimensional overview 10758, How many men women were there onboard 1760, It is easy to spot some obvious features to drop especially features that are uniquely and randomly assigned to each passenger 12025, Stacking Ensembling 6624, Understanding the data at hand 8148, Splitting into Validation 23539, For most image data the pixel values are integers with values between 0 and 255 30937, Visualizing Distribution Of Price Before and After Removing Outliers 7413, How to define a large percentage try 15 first 15647, Decision Tree 43107, This is a common problem that you ll encounter with real world data and there are many approaches to fixing this issue 20411, Checking for NULL values 17540, Chose one hot features 1339, start by preparing an empty array to contain guessed Age values based on Pclass x Gender combinations 25934, Variable Description 4287, Correlation Matrix 15698, Most passengers were traveling without children parents 76 22016, the value 117310 29697, lets take them through one of the kernels in second convolutional layer 8006, SVM Poly 8315, One hot encoding the categorical columns 1232, Largest correlation with Sale Price 31807, try out semi supervised learning with pseudo labeling 3951, Change Datatypes 39301, Export aggregated dataset 28610, Foundation 39114, Data engineering 36193, There are only 2 levels in new customer index a customer is a new one first 6 month And we do not know the index in 27734 cases 18754, Lets perform a left outer join you can perform any joins as per your requirement 11250, Reconstructing train and test sets 28072, String Indexer 26658, Classification report of each class 16035, we split training dataset to 80 for training model and 20 for validate model 37175, Variable importance 620, Child 9776, Parameters to adjust in Kernel SVM are as follows 27768, Examples 34546, CNN Model 16394, BoxPlot 613, study the relation between Fare and Pclass in more detail 18665, RNN Model 14888, Cabin 23649, Predict 3431, we make some dummy variables 6189, The siblings of class 1 survived more than Class 2 and Class 1 1620, Submission 2026, Ensemble Prediction 39067, using quickdataanalysis to create dummies with ease 41636, that we are done with basic data processing let s create a term document matrix for further analysis 15846, Ticket Frequency 27142, Category 2 Structure of Land and Property 9022, Unfortunately we still have not dealt with all of the null values for basement features 9265, I feel nothing requires removal for garagecars 16168, Analyze by pivoting features 24397, TTA Analysis 513, XGBoost 42813, I did not know that we can add the path to environment variables using sys hence I was changine directories but now I have made changes so I do not have to change directories and import detr easily 4264, Utilities 42274, Can t find the difference with boxplot 32358, Building the CNN 17814, Go to top font 2082, I consider the generated feature Surname 37710, CNN Model Structure 28200, Chunking 16967, A quick look over the encoded features 6292, Neural Networks 13030, Cabin 16548, Here s how you can fill missing ages using the Title column as reference 40431, Predicting Using Leader Model 25581, LotShape 39413, Fare 8007, Train Decision Tree Regression 7590, As can be expected from the large correlation coefficient of 0 16659, Exploring Target Variable 24281, Observations to fine tune our models 19898, Top 10 Sales by Shop 11069, Ticket feature extraction 25416, how to extract a date 35896, Great now let s build our simple model by stacking the following 4 layers 16962, Data cleaning 41749, MLP Dropout AdamOptimizer 2318, split out rowsets using sklearn s train test split function 33089, Modelling 39729, After yet MORE reading I ve decided that any model that includes family name massively overfit the data 20139, Let the split be 80 20 train val split 6714, Find Discrete Numerical Features 22769, we create a DataFrameDataset class which allow us to load the data and the target labels as a DataSet using a DataFrame as a source of data 32761, Load Data 7503, The following columns have poor corelation with SalePrice 24893, XGB Classifier 22334, Parts of Speech POS tagging 5004, it s positively skewed and peaky with fat tails or outliers namely to the right 30458, save the newly expanded training data to CS Download this file and plug it in to your existing pipeline as a bigger training set 8161, Outlier analysis 36377, Normalize 23966, No Of Storey Over The Years 3852, we can also use qcut for creating the bins for remove outliers 40203, Prediction 5965, Is alone feature 2775, Gradient Boosting 23223, check how many features are duplicate 36464, Examples of images without any wheat heads 11656, Logistic Regression 8461, Separate Train Test Datasets identifiers and Dependent Variable 20960, Check for Null Values 12700, Age 20648, We use a 100 dimensional vector space 36350, Deep Neural Networks Convolutional Neural Networks 21847, Multilayer RNNs 13023, Pclass 4139, Defining the Sequential Model 42833, Despite the presence of bounds we are going to assume that the transformed data is normal and proceed anyway 38761, The train accuracy is 82 25953, Load Prior data 23038, Week Events and This Week Sales Relationship Analysis 39722, I use Word2Vec to find odd items given a list of items government corruption and peace 36984, Seems the range of logerror narrows down with increase in finished square feet 12 variable Probably larger houses are easy to predict 39714, Training of Word2Vec 23944, 2nd level categories middle level 26591, TASK EXPLORE STORES INFORMATION DATA 35484, Cutout data augmentation 41927, out of all our features we are given 8 object variables 368 integer variables 28716, First look at some informations of our data 4400, Handling Categorical Data 1622, Modeling 37083, Soft Voting 42234, There are 43 categorical columns with the following characteristics 16109, SibSp Parch Feature 14163, Since SibSp include information about the number of siblings and spouses altogether and Parch includes information about the number of nannies we can extract the family size from this info 37876, Lasso Linear Regression 121, Splitting the training data 12128, LotFrontage Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood we can fill in missing values by the median LotFrontage of the neighborhood 9649, Concatinating Test and Train for making Imputing and Cleaning of Data Easier 28788, Most Common words in Text 35944, XGRFBoost 27665, Preprocess the Data 31380, explore Keras ImageDataGenerator 10665, Setup the model 25578, GarageCars 4015, Choose the best algorithm for House Prices Advanced Regression Techniques 15309, Submission 29035, 80 20 training validation split 13091, Correlations in the Data 8784, Grid Params 40039, Searching for an optimal learning rate 38082, VISUALIZATION OF THE DATA 38238, have a glimpse of both the tables 30885, Break the model 24463, ABSTRACT from that paper 15354, try on Pclass 23807, Separate X y 34292, Build Convnet 6883, Missing Values in the column Age can be fixed by imputing values in our case using average of this column 9800, Feature Importance 8971, train tickets 126, Evaluating a classification model font 12385, Plotting a correlation matrix for all ordered categorical and continuous data fields vs Sale Price 22079, Creating submission df to hand in our solution 13684, SibSp and Parch 34620, Cost and Optimiser 11562, We can now plot the learning curves to check whether we are overfitting the training set 14193, RandomForestClassifier 12094, Dealing with Outliers 37575, Categorical occupy the top spots followed by binary variables 30274, Top 20 Countries as per Total number of Test 34169, Sliding Window Method 5465, But what about specific neighborhoods 24540, ind cco fin ult1 is the dominant product 31821, Despite the advantage of balancing classes these techniques also have their weaknesses 42541, Since we are looking at pairs of data we be taking the difference of all question one and question two pairs with this 33888, credit card balance loading converting to numeric dropping 28446, There are 43 such columns 5359, Go to TOC 1853, Prepare Data for Model Fitting 35072, Complexity graph of Solution 5 12221, And there we find that our prediction so much off with respect to the real value was indeed a house too cheap for its size 13531, For all 16 features 40667, Word clouds of Text 42025, Extracting the first alphabet of the first word 37085, Advanced Ensemble Methods 16034, X contain all predictor feature from training dataset 21075, Problem solved 11985, We finally merge those features 11084, remove outliers 38520, Wordclouds 11281, It is very difficult to make inferences from this heatmap 34372, REBUILD MODEL 17521, Model Building 19922, Dealing with Missing Values 16007, Final Analysis 13704, so this is quite interesting 19985, Using self attention 13539, Importing Librarys 19660, If you are interested in running this call on the entire dataset and making the features I wrote a script 8221, We would be replacing the removing values with median values 19666, Collinear Features 41579, we need to add at the top of the base model some fully connected layers 19155, Scale the numerical features 7142, Spliting the train data 20112, Item price trend 2979, Both Exterior 1 2 have only one missing value 36011, Registries 21063, Make it into a dictionary so we can read out each row missing ones we assume are zero like the submissions sample 20113, Month number number of days in a month 32075, Uniform Manifold Approximation and Projection UMAP 36599, We divide the data for testing and training in 90 10 since it is a competition submission However it is generally splitted in the ratio 35819, Remove data for the first three months 38813, Demonstration how it works 34672, Average revenue 12474, Fare 18010, Functions 11893, Output File Submission 10407, Get the data into better working form 18511, It s worth noting that this patient does not have cancer 25761, Make Predictions 1582, We are now ready to fit our model using the optimal hyperparameters 14518, Observations 31549, Garage columns 35351, Building and Evaluating the Final Model 1142, look at the point of visualization 23507, Perform predictions 4295, Fit a regression model with Bloomington Heights as Baseline 30976, we can view the random search sequence of hyperparameters 41768, Write a useful function 40172, The forecast object here is a new dataframe that includes a column yhat with the forecast as well as columns for components and uncertainty intervals 9869, This simplest possible box plot displays the full range of variation the likely range of variation and a typical value 30142, Testing 13219, Feature Engineering data science framework to achieve 99 accuracy 12943, Ticket column 6047, MSZoning FV RH and C are sparse classes have to reassign them Missing 4 times in test 1993, Analyzing the Test Variable Sale Price 21406, Model to Test 6053, Foundation Stone Wood and Slab are sparse 35428, As we are adding the data augmentation and training models several times this process take some time 1540, Name Feature 29896, Data Visualization 40194, Normalizating the Data 9796, The visualization of several numeric features against Sale Price better outlay the relations 9164, ExterQual 19844, The majority of people on the Titanic were between 16 40 years of age 42133, Confusion Matrix 36583, Merge all functions into a model 7829, Store data in csv file as below and submit your output 13740, Creating O P file 29090, USD sales weights 13964, Sex 9153, Deal with Categorical Nominal Variables 37899, Evaluation 15959, Distribution of Numeric Features 32407, Evaluation 16564, Explore 1027, To have a better idea we sort the features according to their correlation with the sale price 37875, Model Comparision Storage 14469, go for analysis for features and there data types 9201, Family 27299, Predict all Country Region and Province States 23581, Re training the model on the entire train dataset x y 1132, Exploration of Gender Variable 24997, Standard scaling numeric columns 40996, Input 35080, Fitting the Scaler to the x train 1695, Examining the Target column 24827, Cropping and Resize 11522, XGBoost scores 38966, seperate id column 15531, Add variable for number of family members on board 38634, First we visualize the first convolutional layer with 30 filters 16045, Data Exploration Analysis 42647, ids with target error 719, This is interesting 22626, Inference 26470, By setting include top False we essentially removed the fully connected layers from the pretrained VGG16 model 15680, Basic Ensemble Modelling 17036, Predict Missing Age with KernelRidge 32892, Ensemble model metrics on validation set 17983, we look at how the gender within the ticket classes affects the chance of survival 17748, Clearly titles like Master applied to small children but all titles have a distinct and quite different median 40713, Normalize Pixel Data 29421, BAG OF WORDS introduction bag words model text A 20bag 2Dof 2Dwords 20is the 20presence 20of 20known 20words 22393, CNN 36293, KNN Classifier 7406, OverallQual GrLivArea living area square feet GarageCars GarageArea TotalBsmtSF 1stFlrSF etc 28152, That s a much cleaner graph 25486, Define training features 6510, Temporal Variables Date time variables 11961, We still some small steps remaining to create the test det similar to train set 30986, The boosting type should be evenly distributed for random search 5897, For test data 37687, Looks like one I guess 6433, There is a subtle difference in operations involving a single column and the last operation involving multiple columns Last operation returns a DataFrame while other operations return a Series If you want the single column operation to return DataFrame you can do like as follows 30894, print 40835, Choosing the appropriate model for regression 19820, Bayesian Encoders 29085, item2vec 5525, Fare Feature 9432, Violin plots 28536, Bellow is the number of null values 16670, Model Training 24062, Plot hyperparameter importance 26706, Plotting Sales of each category across the 3 states 15665, Make model submission 20167, Lets find the score using reduced dimensions keeping the same amount of samples to compare accuracy 25597, MNIST Dataset 27756, Removing contractions 34274, First we ll split the training data into testing and training sets 22000, KNN Classifier 42451, Interval variables 27364, working with item category 16610, Feature Age 7511, Before going to the final detection there s an additional problem to address 20467, Annuity distribution 31598, since we have downloaded the necessary packages lets import the data 12680, Join data files 545, convert categorical to numerical get dummies 14421, Calculate Age per Pclass and Sex for training and test datasets 736, Train Set 32826, We start by importing the libraries to be used and the dataset provided 24182, Better but we lost a bit of information on the other embeddings 36561, Create Model 19937, Since we have one missing value i decided to fill it with the median value which not have an important effect on the prediction 14007, K Neighbors 20549, Blend all the models and let s get the predictions 9608, Getting details of Mr Mrs etc 19605, Univariate analysis of Numerical data 8524, Lot Frontage 15512, Cabin 15487, Neural Network 27495, Load data 3454, That leaves us with 67 1st Class and 254 2nd Class passengers to find Deck vlaues for 41874, Building the simplest Decision Tree Classifier 3609, CONCEPT Variables that are strongly correlated such as with TotalBsmtSF and 1stFlrSF could be an example of multicollinearity which I believe means we need to consolidate these as a single attribute rather than keeping separate 42305, Nearest Neighbors 5998, Hapus outliers 21337, Ki m tra ki u d li u 3710, HyperTuning 21506, Image with the smallest width from training set 13134, We can clearly make out by the first glance that a lot of passengers going to S belong to 3rd Pclass 39026, Cross Validation 301, let s fill the Na with specific values 25962, Reordered Ratio 13504, Feature encoding 41393, Target 30641, Which of the features are we going to use in ML Models p 24438, Embeded 20066, Merging sales dataframe and item shop dataframe into one dataframe by item id and shop id as a key 42060, Using seaborn to display violin plot 7712, Checking Correlations 16947, we create the ColumnTransformer object to fit and transform the data 7056, Type of sale 30754, Fixing max features 6276, Some of these can be merged with other Titles such as Mlle and Ms with Miss 31919, The augmentation generator only use the train portion we got from the split 9066, perform linear regression on all the data at once 29070, Two way categorical features interactions 6997, Area of the Lot 24450, Creating the Model 36716, Picture is more than word 10532, we have removed the outliears manually 32760, Inference 42937, Normalize the data 16561, Fare 30335, We create the validation and train file 33241, that we have a language model trained we can create a text classifier on it 9619, Encoding 8010, First we use ada boost with our random 10962, Lot frontage 31846, create type of item names 1698, Visualizing the locations of the missing data 27910, Prediction 1 Selected Numeric Data 26962, Daily Sales 25878, Bigram plots 28330, identifying the missing value in installments payments 18271, FEATURIZING TEXT DATA USING TF IDF 4680, Filling missing Values 27109, Null Values 27653, One Hot Encoding 26249, Generating Predictions from Test Data 32569, we can evaluate the baseline model on the testing data 27289, Model without intervention 23799, There are many imputation methodology some simple ones such as mean median and mode and some more complex ones such as multiple imputation by chained equations 11115, Label encode features 17765, There are train and test data as well as an example submission file 32990, Define simple classifiers 30580, Function to Handle Categorical Variables 23625, Basemodel EfficientnetB5 40831, A lot of inferences that we have already hypothesised could be verified using the following heatmap and correlation matrix 35549, If we consider only two models then the score vary 10907, Grid Search for Random Forest 15981, we can drop the original cabin feature the temporal one and the numeric cabin feature 40037, Model structure 18227, Utils for models 43386, Uii the threshold is given by a max of 16 pixel that are allowed to be added as perturbation per pixel per image 5890, Test data 15554, K Nearest Neighbours 19397, Visualize a random sample 25749, It does not the network is still very good at figuring out the original size of the train image 19963, Plot learning curves 16002, Fare 19616, One hot encoding 27898, Fitting on the Training set and making predcitons on the Validation set 37298, Lemmatizing the text 7091, From the plot we can draw some horizontal lines and make some classification 24577, Helper Functions 5352, Diplay relationshiop between 3 variables with shape color 27872, Sales and department distribution per store over time 15703, The histogram tells us that most fare values are between 0 100 39878, swarmplot It is easier to interpret than stripplot 2943, Inputing Missing Values 35145, Experiment Size of the convolution kernels 21320, zillow data dictionary 22345, SVD TruncatedSVD 25272, Transfer Learning 22020, Select the most important features 28532, BsmtFullBath 24327, Based on the Feature Importance plot and other try and error I decided to add some features to the pipeline 6385, We can use numpy library to calculate covariance 8410, Year Built Vs Garage Year Built 7528, Feature engineering 11724, ExtraTreeClassifier 13767, Age Fare Pclass analysis 40683, Visualize distance from cluster centers feature space 31806, Get test set predictions and create submission 22092, Define any desired architecture with feed forward function 32286, Display distribution of a continous variable in standard deviation boxplot 27548, Display interactive average based on selection of bar 9744, SipSp 22827, However this doesn t mean that the training set contains all of the shops present in the test set 14003, We can find the people from C embarked port are in higher fare and better ticket class and better cabin than S and Q 27086, LDA with an other way of visualisation 3757, Feature Engineering by OneHotEncoding 15008, Sex 16920, Export data 17866, With the fitted second level model we do the prediction for the test data 4642, Count Plot 16267, Ticket 12144, Basic evaluation 2 32244, it wont always be the case that you re training the network fresh every time 30042, Build SUB Ensemble 16775, Preprocessing Test Data like train data 23172, PassengerId SibSp and Parch data types be kept same 17599, finally fitting our training set and making prediction 3282, Lasso Regression 34040, We have 878049 Observations of 9 variables 21082, Replace missing value with mode 8683, MERGING THE TRAIN TEST SETS 26350, Columns with more than 40 positive or negative correlations with SalePrice 7380, Merging the unmatched passengers using surname codes 27340, Model Layers 42847, New Zealand 9051, I feel like I could merge 3 and 4 into a single category as Above 2 3445, The situation is different for the female titles 433, GarageType GarageFinish GarageQual and GarageCond Replacing missing data with None as per documentation 36938, Correlation Between The Features 40069, Looking at the correlation chart we ought to drop columns that is below a correlation value 18727, let s load our learner from the exported pickle 28315, identying the missing data 11118, Feature importance from RF Model 19743, Pytorch Data Loader 12241, Functions 7842, Well this is something very easy and we have already partly done it 40272, Neighborhood vs Sale Price 33328, Make categorical prediction 31851, Shops Items Cats features 37208, Submission 4446, At my first attempt I dropped all the columns that contain missing value 5345, Diplay Number as scorecard 6365, I tried numberers that round alpha 0 37652, Image augmentation with ImageDataGenerator 21754, Age Study 37614, Converting Categorical values to Numerical Values 41977, To read lines from 101 to 110 5124, Decision Tree Classifier 15170, we shall calculate the F 1 score 39989, Fixing Skewness 42782, Learning rate decay 31291, There is a lot of NaNs in the macro data 16398, Pearson s Correlation Coefficient 35161, Plot the model s performance 28326, identifying the catergical and numerical variable in previous application Data set 28322, Examine the credit card balance dataset 13360, View profile report of test set 39829, convert these predictions into a submiitable csv file 28470, CREATING NEW FEATURES 13394, Drop the Sex variable 20765, let us combine both model predictions 3614, Making sure that the transformation was done correctly 24804, Submission 17705, FILLING THE NAN VALUES IN TEST DATASET SIMILAR TO TRAIN DATASET 11936, Looking at outliers 6166, Fare 7732, Extracting Train and Test Data again 35474, Skin cancer At different Age Group 20098, Item Count by month in each shop and item 15412, have a look if gender had an influence on survival rate 1217, Most common string transform 5602, Outside 36805, With this example in mind we feed it into the tokenizer 27831, Visualization 25890, The Fog Scale Gunning FOG Formula 11431, Bivariate Methods 29317, Women survive more if they embarked from port Southampton or Queenstown 27278, Since CODE GENDER does not appear in the test set we can drop them from the train samples 18955, Display distribution of a continous variable for two or more groups with all different boxplot visualization 40381, Modelling 24378, Save the SVM for later Use 29622, Imputing by its median Because some attribute contains outlier filling with median would have less effect to attribute distribution 18006, Interestingly gender is the least important feature in the model we built which is counterintuitive considering what is largely believed about the tragedy 21261, Save recommender Model 14564, Fare Fare in test data is a numerical variable lets impute with median 3308, Explore outliers with ElasticNet 25993, Appendix PCA of VGG16 Embeddings 8982, MSSubClass 37303, Tri grams 21443, Prediction 43138, How d We Do 24436, Chi 2 2749, Categorical values need to be treated differently 23802, Modelling with Xgboost Blackbox Model 6466, Join the numerical and categorical pipelines 34042, The Dates column type is String It be easier to work with by parsing it to Datetime 16981, GridSearch Hyperparameter tuning 39095, The Flesch Kincaid Grade Level 34006, temp 18019, Prepare training data 24799, Wavenet 35049, How does an image of this dataset looks like actually 7427, I am not going to use regression models for now because of the assumptions they make on the data 2541, An example with another activation function Leaky Relu We can create this new function with Relu 9223, Random Forest based Model and Prediction 28244, Lets read all the fiels and have a glimpse of data 22482, Andrews curves 7053, Type of heating 37809, Import Libraries 28724, Items category 8016, Pclass 2467, LASSO Regression 26738, Plotting sales over the days of month 29876, Commit now 192, Ridge Regression 22043, Price of the house could also be affected by the availability of other houses at the same time period 24371, The distribution is right skewed 39057, get the validation sample predictions and also get the best threshold for F1 score 28234, Split the train and the validation set for the fitting 19704, Modeling 24399, tweak all the layers 26334, Bag of Words Vectorizer 9395, History storage 1018, criterion A function which measures the quality of a split 8158, By looking at the correlation plot we note that the following features are highly correlated to SalePrice 9188, Average Age per Class 31294, Yet we have to write another algorythm to purify that set and get the final time categories for macro data 15881, Make Predictions on Test Set 30933, In one sentence we can also notice that there are http addresses within the data 20299, Fare Range 33733, Writing a custom dataset for train and validation images 43003, Data cleanning remove irrelevant 40545, RandomForestClassifier 43285, Avalia a m trica da competi o em todos os dados a partir das previs es OOB 13433, Sigmoidal 2795, Finalise Trained Model 16552, let s do it 3573, I think that outlier 34026, Delet count log count boxcox columns 12334, Alley 5523, Embarked Feature 19838, Box Cox Transformation 22503, defining Loss function for gradientDescent optimizer for getting optimal value of logit variables 17669, Tickets which are pair means that you are on the left of the boat 35923, Compile and summarize the model 15353, let s do some partial plots 16897, In order to eliminate all the titles which have really low occurances we should recategorise them 14466, back to Evaluate the Model model eval 10234, only one value is missing from Fare column which we can fill by median fare 16864, Preparing to prediction including FE 8947, Fixing Electrical 32735, Clearning data and manual FE 39727, Titles 9681, check average out of sample score 20930, open the session 25400, NORMALIZATION 10252, Go to Contents Menu 23916, Readability features 4318, As we look closer it appears that there are more survivors around the higher fare price range as opposed to the lower ones But this we already know from the other features 1584, Our last step is to predict the target variable for our test data and generate an output file that be submitted to Kaggle 37829, Logistic Regression 21735, Before submitting run a check to make sure your test preds have the right format 17603, KNN 14996, Distribution 10814, All small categories should be taken out 37920, Target Model Creation 20961, Splitting Dataset into Training set and Test set 13421, Bivariate Analysis 17569, Support Vector Machine 3250, Creating functions for effecient data visualisation 15132, Engineering Feature 23764, First look at cat pictures 28886, Feature Space 43294, Removendo a Coluna Day 21132, now add the new interaction to our main categorical data set 3833, categorical features 36478, To construct the private test set I tried many different things and algorithms 28485, MEMORY CONSUMPTION 22001, Notice that the dataset contains both numerical and categorical variables 41573, Visualizing some Random Images 15308, Training XGBoost Model Again 22685, Main part load train pred and blend 40656, The next question is how to handle the object data types 22911, Confusion matrix 27161, Category 12 Sale Type and Condition 33716, FEATURE PARCH 41953, Convert text to lowercase 287, Ticket 39241, Visualization 10203, One hot encoding 40324, URLs 22758, DATA AUGMENTATION 24761, ElasticNet Regression 18593, By default when we create a learner it sets all but the last layer to frozen 11131, This chart does not look linear or at least the line is not matching the data across the entire x axis 13174, We have 929 unique values in Ticket let s just droop out this column 12804, Import Necessary Libraries 6998, Masonry veneer area in square feet 28225, Here we construct the model architecture 16847, The Fare variable 30362, there is data with United States by Province let s make models 24420, Floor of Home 37476, One hot encoding creates sparse vectors 11694, Single Imputation for Numeric columns 13719, CONCLUSIONS 24103, Dropping the less important features 4849, Kernel Ridge Regression 21233, Build our base model 22842, Aggregating sales to a monthly level and clipping target variable 36990, Number of products that people usually order 29131, Run the code 10408, Plot all significant features along with a regression line 41402, AMT GOODS PRICE 14934, The format of submission 26132, Modeling font 42068, Using sklearn lable encoder for pre processing 20917, Crosstab 25586, Modelling 10229, For this data set I be using Random Forest Classifier we can use other classifier models but for the sake of simplicity I use only one model here 41612, Check the unique values in that column to make sure that all numbers 0 30 are accounted for before removing the NaN rows 21921, Thus the model generalizes best score when the number of features are 270 31796, by using functional module we can access to all layers in the backbone which is evident when executing model 12038, First of all I m gonna look how many variables have less than 50 missing values and fix it 21586, Count of rows that match a condition 29174, Visualize some 21507, I would like to play with some augmentations from 16270, The Pipeline 22152, Not all features are like that though 39775, Importing utils from sklearn 20963, Encoding Categorical Data into Continuous Variable 21228, Define loading methods 41564, See how does kernel PCA fare to reduce complexity we use only 10k rows 25051, Training 12147, Training the model 3 20242, We also encode the sex column 43130, Chosen Examples 23269, Name 18704, A new CSV file cleaned 16854, Lets double check for any categorical data 9485, Validation Curve 39985, SalePrice vs GarageArea 32315, From the df train dataframe I have created a dataframe X which contains all the features and a numpy array y which contains values of survived passengers 23478, Exporting output to csv 41583, In this section I have done fine tuning 15688, Convert categorical columns to integer 12376, In this we have added an extra visual column for the box plot 24388, first fill all numeric values with the median 4333, It appears those who embarked from Southampton took the biggest toll followed by Cherbourg and then Queenstown This might need further investigation Does it have to do with the class they travelled in or if they were in a cabin or not 8585, Separating target feature 3901, M Estimate Encoding 24451, Model Evaluation 1899, GaussianNB Model 10028, LightGBM 1264, Fit the models 12210, Putting everything together 9642, 10 Excellent Quality therefore Higher Sale Price 31693, Missing Data 631, Very much so 42260, probably the same people that we just determined were new customer double check 13120, feature Analysis 38526, Loading the dataset 9704, Missing values in categoric features 23053, we are with 98 accuracy goood 24885, It is integral to merge both train and test datasets before feeding input to the model 17453, XG Boost 34073, 1st class passengers are older than 2nd and 2nd is older than 3rd class 23081, Pairplot scatter matrix 25010, First Time Sale 28448, COLUMNS WITH MISSING VALUES 2522, Hyper Parameters Tuning 14870, Who was alone and who was with family 19056, We can now easily collate all the data into a DataBlock and use fastai2 s inbuilt splitter function that split the data into train and valid sets 1231, Numerical values correlation matrix to locate dependencies between different variables 19811, If we performed One Hot Encoding in the variable Cabin that contains 148 different labels we would end up with 147 variables where originally there was one If we have a few categorical variables like this we would end up with huge datasets Therefore One Hot Encoder is not always the best option to encode categorical variables 37048, Implement the best model 41407, AMT INCOME TOTAL 21620, Create rows for values separated by commas in a cell assing and explode 37794, Drawing predicited data 33814, Make Predictions using Engineered Features 33356, Timeseries decomposition plots monthly sales 4958, LightGBM 12121, MiscFeature data description says NA means no misc feature 3913, MSZoning RL is by far the most common value 8703, For handling skewnesss I take the log transform of the features with skewness 0 33758, MODEL Creation 32079, Figure 3 Distribution of the number of unique levels for a categorical variable 32883, Normalizing features 37463, Evaluate the model 16021, Very clearly there is outlier 200 30571, Correlations of Aggregated Values with Target 2197, GradientBoostingRegressor 17787, let s set the female Dr as one of the female tipical roles 27141, In MSSubClass The Newer 2 STORY and 1 Story PUDs have on average higher sale price than the others 14199, Feature Engineering 8525, Garage Features 42938, Saving the data to feather files 25300, Here we go 657, Test and select the model features 40700, Selecting and Engineering Features for the Model 18320, Train Validation Test Split 29828, Basic DNN 3482, This model appears to do a slightly better job at picking out the true survivors 15991, Random Forest 31945, Check missing value 22652, Gradient Descent 8511, There you go SalePrice looking off the charts 13053, ExtraTrees Classifier 12203, Please note that this transformer takes a parameter that determines whether or not to create a new feature 27007, Model 4339, Second class 37392, SalePrice is not normal 28682, Miscellaneous 24151, Morning and Afternoon Products 7338, KNN 19658, Feature Primitives 27185, It is also worth pointing out that the actual number of test rows are likely to be much lower than million According to the data page question pairs data most of the rows in the test set are using auto generated questions to pad out the dataset and deter any hand labelling This means that the true number of rows that are scored could be very low 23741, Feature Scaling 30693, Testar os Modelos 28196, Stemming Words 7273, Fare Feature 13569, Parch feature 37647, Adding no of photos no of words in features and description 37087, Stacking Or Stacked Generalization 38898, Split Dataset in Batches 9861, Total data table s numerical part value counts values 27002, Untreated text 25255, Show some samples 3969, XGBoost 25598, Multivariate analysis 12329, GrLivArea 10776, not bad at all for a KNeighbors model on such complicated dataset 17932, female male is True 7554, Gradient Boosting 4321, Combining Fare and Pclass 12787, Evaluation report 42071, Submitting 35646, The below code snippet is taken from my previous kernel and also from this wonderful kernel by SRK 566, eXtreme Gradient Boosting XGBoost 13122, Using hue 11291, Feature Engineering 17372, Embarked C 18465, Load the datasets 8677, We can drop the Id column as the frames are already indexed 39088, Training 12673, Transforming instead of skewing 27252, Construct the Models 41235, Clean data 24302, Before training the model we need to compile 26057, We can then loop through all of the examples over our model s predictions and store all the examples the model got incorrect into an array 23943, 3rd level categories 36959, K Fold Cross Validation 40150, There re 172817 closed stores in the data 34161, Understanding russian using googletrans 13133, Survival by Embarked and Pclass Interesting finds 28727, Total revenue Representation of total sales 40488, Ensemble Stacked Generalization 9338, Final tips 13215, A beathful curve our almost 90 of AUC in class 0 and class 1 or model are very stable 27447, It s still unclear what the platform means but it s possible that it s things like computers phones tablets etc 38212, OneHot encoding 22280, Keep in mind that the model accuracy could be improved by finding titles for each of the passengers 17737, The vast majority of recorded cabin numbers came from first class passengers 23174, Model Building and Evaluation 12911, Preview test set 6422, There are outliers in the dataset these be treated in the data engineering section 16434, Gaussian Naive Bayes 11658, Suport Vector Machine 19757, Data dimensions 24952, Test set 14527, Fare values are highly skewed and hence need to be treated with log transformation 2325, Using GridSearch we can find the optimal parameters for Random forest 17869, Feature Exploration Engineering and Cleaning 8364, Check if we disrupted the distribution somehow 4605, Other features 2802, detect outliers Detect Rows with outliers 5978, AdaBoostClassifer 16522, Handling categorical features 35098, documents topics csv 10340, Based on the previous correlation heatmap GarageYrBlt is highly correlated with YearBuilt so let s replace the missing values by medians of YearBuilt 2399, Common ways to encode categorical features OneHotEncoder OrdinalEncoder 32868, Dataset after feature engineering 19954, Cabin 312, Fare 15138, Engineering 37027, More details on brands with a treemap 22137, GarageCars Size of garage in car capacity 5845, Feature to Feture correlation 26811, Save all states for honest training of folds 16989, ExtraTrees Classifier 26897, Score for A7 16073 30559, Checking skewness of all the numeric features and logarithm it if more than 13720, FEATURE ENGINEERING 14189, KNeighborsClassifier 18922, compare the accuracies of each model 13898, Plot of sizes of differnt age groups 28127, Stopwords Removal 15370, Embarked Mapping is required to convert string to numeric values of Embarked column 5349, Diplay time series with scorecard in it 25979, Load Models 13836, Which features are categorical 33103, Fit the models to the whole dataset 9216, Survivor by Fare Price 38961, Training Function 29227, Linear Regression 11244, Right tailed 3615, let s standardize the numeric features 2781, let s ask dabl what it thinks by cleaning up the data 8535, UNIVARIATE FEATURE SELECTION 11403, Selecting a Single Column 10629, DecisionTreeClassifier 25423, Most of the dates overlap 18735, Max feature and min samples leaf 2372, Feature selection with Pipeline 17469, Fare 40829, A non linear relationship between temperature and day of the hour according to different seasons is evident from this chart 4111, Merge the training data and test data 226, Model and Accuracy 40461, Exterior 25594, Fully Connected Layers 3231, Violin Plot 28796, For Full Understanding of the how to train spacy NER with custom inputs please read the spacy documentation along with the code presentation in this notebook Follow along from Updating Spacy NER 43305, Investigate numerical columns 42657, After this split we can now draw violin plots 14931, Model Comparison 598, This is a tricky feature because there are so many missing values and the strings don t all have the same number or formatting 18659, Build Model 11219, We have bumped up the predictions and they look correct so far now to verify on the previous chart 408, Adaboost 42075, Five models are fitted and found to have different levels of accuracy 21195, Compute the loss 16279, The Final Model 2393, Difference between Pipeline and make pipeline 8117, Random Forest 19711, Preparing Y train 43375, Training the Tensorflow graph 11848, Label Encoding Manual 1190, we resplit the model in test and train 5620, Using sklearn preprocessing LabelEncoder 32988, Neural network 13892, Passenger s Name 15363, parch The dataset defines family relations in this way 5982, Random Forest Classifier 21799, Comparison table 5954, Edit font 2358, Parametric Generative Classification 951, Helpers via Python Classes 19142, Model 3 Input Sigmoid BatchNormalization 512 Sigmoid BatchNormalization 128 Sigmoid output 12308, Pearson 1 Spearman 1 Pearson 0 26838, Year to Year Trend 4949, let s get all the dummies 20286, Slicing Intials From name 27360, Testing the model performance after trimming all values greater than 20 and less than 0 2684, Forward featrue selection 39253, Export data 28521, YearRemodAdd 3787, Pairplot 34260, Plot Lags 41371, Sale Price FR3 CulDSac FR2 Corner Inside 2248, Keras and TensorFlow 26055, Afterwards we load our the parameters of the model that achieved the best validation loss and then use this to evaluate our model on the test set 1250, plot the SalePrice again 14426, go to top of section engr 15944, Name 7756, Transformation Pipelines 15319, Since we have explored all the features in our dataset now we shall draw close comparisons with SURVIVED feature to help us draw some inference 2949, Model Parameters tuning with RandomizedSearchCV 17809, Split the data 22710, Fetching the variance ratios for PCA over the given dataset 11412, Use Case 4 Tableau Data Visualisation using Sankey 24704, Create submission 6211, Random Forest Classifier 31400, Create Predictions 25732, set Loss function and optimizer 2513, Linear Support Vector Machine linear SVM 107, name length 22821, we inspect the item price field for low priced outliers 28732, TOP 25 items Solds 2818, Train a quick randomForest Resressor to check the feature importance 13083, Creating the train and test datasets 17946, C 29005, Hope you enjoy 1334, We can replace many titles with a more common name or classify them as Rare 42187, The predict method return a vector with the predictions for the whole dataset elements 32827, In this part we define some functions which we can use later to make the process smoother 23246, We fill the Age s missing values with median 14265, Radial Support Vector Machines 1338, Completing a numerical continuous feature 30390, Tokenizing 16951, EXPLORATORY DATA ANALYSIS 4159, When doing count transformation of categorical variables it is important to calculate the count over the training set and then use those numbers to replace the labels in the test set 37099, Although Zscore and IQR methods suggest several outliers for while I m going to focus on outliers with remotion recommended by the dataset author 7484, Fill the missing fares with the median 2889, First find out of missing values in each features 32546, SalePrice Distribution 28099, Fit the Model 26836, Monthly Demand 41787, Data augmentation 14833, Hyperparameter Tuning Grid Search Cross Validation 33337, Quick look at items category df 3172, I ve augmented the Titanic Dataset 8192 to be able to compare the performance 22649, Log Odds 33264, Fit model 30263, The F1 score 12195, A Pipeline step by step 20505, We also need to pad the tweets that are less than 220 words which is essentially all of them 22114, Choose alpha with better score 11887, KNN 33513, Germany 13613, For target mean encoding we are going to replace categories with their mean target 20532, create a few more features by calcualting the log and the square of the features 33574, Data Preprocessing 9053, This looks rough 7902, First we run this loop to detect the correct number of Nieghbors in KNN 7469, Importing the packages that I need 16697, Convert females 1 and males 0 3438, Others like military or noble titles we ll group together and create indicators for 38929, Deleting columns which have very high frequency of Na 8127, Relationship with numerical variables 36989, There is no missing data in order products all dataset 8845, Machine learning models generally want data to be normally distributed 42769, Name Lenght 9799, Stack Model 1 38560, Modeling and Prediction 3315, Ridge 24533, Number of products by customer index 43159, Define the model 33140, check if the model looks the way we want 12834, We already have Survived column so we drop the colom with name survived or not 5131, Relationship between values being missing and Sale Price 8812, Some usefull information 9836, Logistic Regression 21366, See Where My Model Scored Jaccard 0 for Positive and Negative Sentiments 40088, Scaling shuffling and splitting 12175, Data analysis 15862, Model training and score prediction The cross validation strategy is a k fold scheme For each fold we make the trained model predict probabilities for the test set With 5 folds this means each row in the test set recieves 5 predictions probabilities from 5 versions of the model fitted on slightly different training datasets This gives a better score than just training on the full train set and predicting for test 36115, Lets deal with numeric null values now 29057, Source image 23661, Rolling Average Sales vs Time Texas 37764, Technique 9 Memory profiling 7574, Distplot for SalePrice and SalePrice Log 36001, Duplicated columns 27535, Display the confidence interval of data and used on graphs to indicate the error sorted 28178, Entity Detection 5686, Cabin 10849, Encoding the finalized features 36406, There s quite a few variables which are probably dependant on longtitude and latitude data 17628, Embarked Cleaning Fill missing Embarked values by taking mode of Embarked 10397, Prediction on Stacked Layer 1 40666, Importing Data 35560, Classes 0 2 can be predicted extremely well using just the image meta data 9763, Encoding Data for Analysis 21166, Label Encoding 18259, Quadratic Weighted Kappa 32370, Diagnosis Distribution 34530, MostRecent 27336, Normalizing Data 35325, Submission 41470, Feature Age 9013, Functionality 37326, Selection of filter parameters for convolution layer of New Module 28821, Sales per week of the year 32175, EXTREME FEATURES EXAMPLE 24526, Customers attraction by channel 12076, Feature Scaling 32318, Since the test 9360, Drop Parch SibSp and FamilySize features in favor of IsAlone 7337, Build Model 27204, Age 7432, Compared to random search model grid search model performs a little better 26457, RF Prediction for test dataset 7336, Convert values of Embarked and Ticket into dummy variables 16340, Gaussian Naive Bayes 28454, Analyzing columns of type object 27538, Display the distribution of a continous variable 11004, At this point we can create a new feature called Family size and analyse it 255, Model and Accuracy 18902, Pclass Feature 32198, Cleaning Item Category Data 19323, Data Normalization and Cleaning 33342, Viz of sales per week month of shops and item category columns 7956, Use best hyperpameters and train best model 38763, The train accuracy is 81 13411, Recall 25722, Import Library 2676, Fischer Score chi square 12344, GarageCond Garage condition 6316, XGBoost 7627, Loop over GridSearchCV Pipelines Ensembles 31002, One of the EDA kernels indicated that structuretaxvaluedollarcnt was one of the most important features 39042, This should be further investigated I reckon but let s now forget about group 0 and consider the buildings with most entries having proper ids 24000, Stacking 42099, Converting y train to categorical variable you ask why 16579, Fill Missing data 20759, BsmtFullBath column 31569, transform is ToTensor 19877, Min Max Scaling 7506, Most missings can be understood from the data description 35937, Ticket 750, Create the model architecture by compiling input layer and output layers 4857, Stacking 9518, PClass 10581, Random Forest 23604, the more documents you have the better 12894, Age 29524, Naive Ba 20407, Distribution of data points among output classes 21472, As more and more examples are generated we need to generate textID values for them 34162, It is clear that most sales are related to eletronics especially videogames and consoles 1419, Free vs Survived 6094, take a look at garage area 41742, Prediction on validation dataset 5603, Find 31244, model train 9778, Random Forest 14542, There were total 1309 passengers onboard font 15550, preparing Data 14876, Looks like there is a general trend that the older the passenger was the less likely they survived 33761, Examine Dimensions 16683, Distribution of categorical variables 16188, We can also create an artificial feature combining Pclass and Age 20231, There are 177 missing values in Age column we impute them in Feature engineering part 12270, XGB 38648, Enumerating and Converting 21389, Predict on test dataset 16095, Name Feature 13440, Sex Females have more chance to survive 14123, Sex and Pclass 21842, Vanilla RNN for MNIST Classification 2404, That is a very large data set We are going to have to do a lot of work to clean it up 20138, One hot encoding the target variable e y train 33789, Those ages look reasonable 5676, As the chance of survival is more for Pclass 1 we change the numerical values so that more weightage is added to Pclass 1 instead of Pclass 3 32888, Create new datasets with the predictions from first level models 11619, Age 20 30 should have highest survive rate since they are in the healthiest age range 12160, Max Features 24754, Class imbalance 42419, Build Year 10863, Taking a look at the target variable 11851, Importing libraries 3896, Scatter plots 22511, look at the straight average 7064, GridSearch for Ridge 12256, Evaluation 22670, Most Common Trigrams 37912, Train Test Split 38138, Generate test predictions 31885, normalizing data 1591, Age 10082, We have now removed the skewed data and we can now apply the normalization if needed but since all values are in same range after log we can go without normalization 14342, Try SVM 28019, RF 10414, Create pipeline with models 25659, Conclusion Work in Progress 29380, Prediction on Test Data 23911, First scores with parameters from Tilii kernel 20091, Replacing outliers with value of the row having similar condition 2484, Sex Categorical Feature 27160, PavedDrive Paved driveway 19466, begin by printing a preview of the dataset and look at its size and what it contains 10453, XGBoost 24711, lets look at the main variance directions of the PCA 6701, Our dataset features consists of three datatypes 4462, Combining the 2 datasets 13795, Voting Classifier 41872, Showing the best parameters combination 7972, Create new feature combining existing features 31022, Remove Emoji from text 37504, now randomly i am try to find out the relation with saleprice for different categorical data 14925, Logistic Regression 34223, DataFrame Format 5438, Are we done 36198, let s tokenize the first tweet again and add the special tokens as well 20831, We ll replace some erroneous outlying data 14288, ROC Curve 15639, Re evaluate the on new features 29900, Designing Neural Network Architecture 30467, Tune multiple models simultaneously with GridSearchCV 7601, SalePriceLog as target 8735, Sales 28219, let s reduce memory usage 33155, we calculate the orange area by integrating the curve function 41159, FEATURE 5 19936, Fare 21523, now let s implement a simple convolution depending on the parameters we have chosen earlier 5173, Linear Regression is a linear approach to modeling the relationship between a scalar response or dependent variable and one or more explanatory variables or independent variables The case of one explanatory variable is called simple linear regression For more than one explanatory variable the process is called multiple linear regression Reference Wikipedia 2337, Discriminative Models 7531, We can fill missing age with mean but age varies for each Pclass so filling missing age with mean not be proper Lets fill Age according to Pclass 20697, How to Save and Load Your Model 40987, Another but not as useful way 6949, I choose most import features 27351, x are best paramaters with the least aic score 434 27442, Output 10925, Calculating the diameter of the graph 18308, use item first month to create new item feature 22768, We need to create Field objects to process the text data 15777, Usually this is the one that saves us 864, Embarked Survival rate lowest for S and highest for C 23961, Top Features Selection 10662, Ensembling 34439, Build and train BERT model 424, This distribution is positively skewed Notice that the black curve is more deviated towards the right If you encounter that your predictive response variable is skewed it is recommended to fix the skewness to make good decisions by the model 12015, what random forest done is that it combines the predictions of several independent decision trees which helps in reduce overfitting problems 21518, Fitting The Model 23542, Split training and valdiation set 35519, One hot encoding 5546, Split Data 27667, t SNE 40620, let s compare 11916, Creating Categories for Fare column 568, CatBoost 22717, Creating the image matrix 20773, Perhaps a non linear technique yield more insights This also helps to collapse the information from all 50 dimensions when we apply the T SNE technique to this LSA reduced space 17016, Create variable ticket type 13608, We can also encode categorical variables with their frequencies 435, BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF BsmtFullBath and BsmtHalfBath missing values are likely zero for having no basement 103, Sex is the most important correlated feature with Survived dependent variable feature followed by Pclass 4983, Create code variables for the categorical data 37057, Process SibSp Parch 21491, SAVE DATASET TO DISK 26618, We set a function for parsing the image names to extract the first 3 letters from the image names which gives the label of the image It be either a cat or a dog We are using one hot encoder storing 1 0 for cat and 0 1 for dog 34719, Selecting proper categorical features 27646, our last classifier be a Naive Ba Classifier 32099, How to remove from one array those items that exist in another 1307, Observations 5679, Create Age Null columns to indicate NaN values 4489, XGBoost 43355, Submiting our output 11408, Reduce the mean square error Model by using Imputation 32674, Six regression models covering a variety of regression strategies techniques cross validation capabilities and regularization features have been elected in this exercise 26834, Plot the count 35256, In below code please go through comments which i have mentioned between codes to identify variables progress line by line 1824, Using cross validation 31797, Just to make sure that we have the correct weights 9070, It looks like the data is positively skewed 31711, The original decode predictions tries to download imagenet class index 28750, fit and calculate the log mean errors for each model 36471, Images from INRAE 14348, Read In and Exploring the Historic Data EDA 18072, plot some image examples 11810, We have successfully imported everything we need to solve this challenge 28314, identifying the catergical and numnerical features 27177, Define a function that helps us in creating GBM models and perform cross validation 5031, Kernel Ridge Regression 25869, Importing Dataframes 19879, Robust Scaling 22216, Setup dataset 11082, Stack predictor 33741, Submission 22091, Architect our Neural Network 32798, Here I would like remind you that for stacking you SHALL use consistent fold distribution at ALL level for ALL your model 17656, analyse what are the features that makes this much difference 26361, As I ve setup the NetworkVisualiser class with default values appropriate for the MNIST dataset all I need to specify when initialising the NetworkVisualiser object is the list of layers 28056, Exploratory Data Analysis 37336, predict and submit 13796, Prediction 16890, New Feature FareBin 28748, there are missing values for LotFrontage and MasVnrArea in both train and test data 41915, The learning rate determines how quickly or how slowly you want to update the weights 7580, Dropping the column sum 1SF 2SF LowQualSF again since it already exists as GrLivArea 22896, There is no common top 10 keywords between disaster and non disaster tweets 3457, Here are the parameters for the model with the highest accuracy on the training set 27223, Masking 1258, Numerically encode categorical features because most models can only handle numerical features 22249, Cross Validation K fold 3152, The script begins with the usual imports 17605, Perceptron 25262, Max Min Height and Width 12137, Training the model 2 36802, Named Entity Extraction 22059, check how the difference of length relates to the length of the tweet s text 31746, RandomGreyscale 32806, Level 2 XGB 42035, Groupby Mean 30399, Tweaking threshold 21509, Augment the images with CLAHE 11945, Aim here is to create multiple features from highly correlated features which might help enhance the prediction 3430, replace the two missing values of Embarked with the most common value S 41538, Dictionary learning 941, Da Double Check Cleaned Data 26997, Now let us deal with special characters 36995, Which products are usually reordered 43065, Distribution of min and max 19418, Another handy thing to know is how does the RoBERTa tokenizer handle concatenated sequences 30376, Train 25871, Target Value Distribution 10418, Partial Dependence Plot PDP 8593, Dealing with Categorical features 3621, then encodes qualitative variables based on the mean of SalePrice for each unique value ordered starting at 1 6203, XG Boosting 29863, in operator 30594, Calculate Information for Testing Data 23680, Loss 26985, Build model 7631, blend 2 gscv Lasso and gscv ElaNet 6040, How is look like our predictions 21170, Learning Rate Schedules 765, Missing Data 27101, Submission 11401, Building the logistic regression model 21057, Training Set 10604, Split data into training and validation data 9048, ExterQual 22943, Nice visualize the features we made so far 5571, let s deal with Categorical columns 10692, Age processing 13871, We further split the training set in to a train and test set to validate our model 23030, Sell price and value relationship 19400, We have quite balanced data 23224, Store the duplicates into a variable 33730, Load train and test file 13896, Most of the models require the data to be standardised so I am going to use a scaler and then check the scores again 25388, Visualize some examples from the dataset 25321, Generate test predictions 38733, Again it looks like there are no data errors just some passengers who got a free ride for whatever reason 14171, For this dataset we examine the effect of features on 5 different models br 1355, Logistic Regression is a useful model to run early in the workflow 17472, RandomForest 7071, The Age Cabin Embarked Fare columns have missing values 4034, Outliers Removal 36142, let s set up the hyperparameter search for the RandomForestClassifier using RandomizedSearchCV 36101, which embedding do we use Well there are a couple of ways to combine different embeddings one that comes naturally is taking the mean across all of them 36770, Making Predictions on the Validation Set 25888, Score Difficulty 20529, Some numerical features are actually really categories 17458, Sex 21459, Since the label is in the form of dataframe it needs to be converted into array 28148, Build Knowledge Graph 25226, Create a dictionary to keep track of scores for each model and compare later 2938, Feature Engineering 24823, BERT Modeling 920, Model Building and Evaluation 7871, The first features to work on are SibSp and Parch 16121, Gaussian Naive Bayes 24252, Observations 29062, Additional functions that might be useful 41617, Whoops 608, We learn 6687, Pair Plot 8603, Null in train data 22864, Convolutional Neural Network 18758, The get segmented lungs function segments a 2D slice of the CT Scan 7029, Interior finish of the garage 31, we extract the features generated from stacking then combine them with original features 17923, Even after using 18469, After reading the descrition of the this task Rossman clearly stated that they were undergoing refurbishments sometimes and had to close 32687, Now we divide the training data into two sets one for training and one for validation 22252, Naive Beyes 23881, From the data page we are provided with a full list of real estate properties in three counties data in 2016 24236, Prepare generator for test set and start predicting on it 20513, The info method is useful to get a quick description of the data in 33869, Model and Parameter Tuning 3646, Grouping ages in Age feature and assigning values based on their survival rate 25406, DROPOUT 9268, I tried to remove a few least correlated features from the training set 29536, we are going to normalize our data 9850, Data preparation 19894, Initialize the model 24918, Decomposing the data 37782, Submission 17271, Support Vector Machine 35457, Visualize the Chunk of Non Melanoma Images 3068, Fare 36078, Configuration 13356, Print variables containing missing values 25059, I finally understand 36807, Now we go a step further and count the number of nounphrases by taking advantage of chunk properties 32964, Insights 42931, Saving the number of rows in train for future use 36666, We have 4825 ham message and 747 spam message 33759, Final Submittion 1083, Linear model without Regularization 4892, Whoops Apart from Mr Miss Mrs and Master the rest have percentages close to zero 24686, Number of parameters 28271, Converting the train and test dataframes into numpy arrays 9384, check the houses who have zero basement area 17767, Check for missing data 21330, Di n t ch 29826, Pre Trained FastText 9233, Train with Remaining Data 75 15 2823, creating a function that evaluate the algorithmes performance Learn more about OOB score 16646, Holdout Prediction 43158, Display one image 29884, Word Cloud visualization 28589, KitchenQual 4622, There are about 19 missing values 2544, center Predictions bis 1619, ROC Curve 16453, Cabin 6142, Split years of onstruction into bins 29762, also visualize the model using model plot 18980, Display more than one plot and arrange by row and columns 22832, Great that worked just add these codes to the shops dataframe 7713, List of Highly correlated features Here we ll visualize them and clean the outliers 35686, LightGBM 24462, CenterCropping height width 256 26901, Using Gradient boosting 20409, Checking for Duplicates 38100, Downloading MNIST Dataset 39434, MY PART STACKING 25907, Generating tweets about not a real disaster 43293, Criando um Set de Valida o Melhor 16265, Name 1975, Hyper Parameters Tuning 41658, Distribution of missing values 13696, Submission 3759, PCA Principle component analysis 23170, Dropping Features 41399, NAME TYPE SUITE 6732, GrLivArea Vs SalePrice 11210, add the previous averaged models here 30359, Predict all country greater than 1000 13291, Tikhonov Regularization colloquially known as Ridge Regression is the most commonly used regression algorithm to approximate an answer for an equation with no unique solution This type of problem is very common in machine learning tasks where the best solution must be chosen using limited data If a unique solution exists algorithm return the optimal value However if multiple solutions exist it may choose any of them Reference Brilliant org regression 12926, Here 0 stands for not survived and 1 stands for survived 35590, We split the data into the train and validation sets 27614, Finetune Vgg16 16098, After that we convert the categorical Title values into numeric form 14471, nullity analysis 20395, Gaussian Naive Bayes Model 29763, Run the model 28134, Designing our model 34154, Early Stopping with Cross Validation 11320, Parch 9967, Correlation by Lot Area and Price 38817, Replace all missing values with the Mean 27988, Partial Dependence Plot 27257, have a look at our feature importances 8540, RANDOM FOREST 25034, there are no orders less than 4 and is max capped at 100 as given in the data page 25412, TRAINING 37327, Selection of kernel size parameters for convolution layer of New Module 26762, Build Model 10339, Since MasVnrArea only have 23 missing values we can replace them with the mean of the column 10746, I am thinking of better ways to visualize the relationship between the SalPrice and categorical features any suggestion be greatly appreciated 34355, Understand our predictions 3502, Model 5 Gradient Boosted Tree with Reduced Parameter Set 4092, Older versions of this scatter plot had a conic shape 3461, Here s the contingency table for Deck and Pclass again 6334, Data cleaning 8974, if you search from internet embark should be S just have a check 10828, We have tickets assigned to more than one person 22591, There is one item with price below zero 12674, Joining newly created dummy data columns 24556, let s check the products share of different cases 21670, XGBoost 21325, Tr c khi quy t nh lo i b m t s feature c n m b o r ng m t c ch NaN th c s mang ngh a l Kh ng 10884, Passenger Name is a categorical variable and it is obviously unique for each passenger 28474, Most of the houses have ATLEAST ONE of the mentioned exquisite features 23320, Add previous item price as feature Lag feature 15836, Embarked 4488, Random Forest 38063, REMOVE TEXT WITH NULL TEXT CLEANED ONLY IN TRAIN 31520, We implement random search CV technique as this search the search space randomly and attempt to find the best set of hyperparameters 16084, Relationship between Features and Survival 27178, Tuning number of estimators 35814, Interaction features 40986, And now using groupby and aggregate functions together we get 37435, WordCloud for tweets font 5418, Features Engineering Data Munging font 21966, lets understand the data before any strong Exploratory Analysis strong p 36718, Random Rotation Shift Affine Transformation 36872, Keras 2 hidden layers 11355, SalePrice is the variable we need to predict let s do some analysis on this variable first 8304, Model 1 Decision Trees 19086, Age Histogram based on Gender and Survived 4449, Select an algorithm 37824, Tokenize Stemming 14926, Random Forest Classfier 26580, This our model gives this image a high probability of being a dog 32678, Interestingly weights associated with individually best performing regressors have their weights reduced during the weight optimization process 2661, Voila 24576, Constants 22647, For logistic regression we don t need to standardize the data 42103, Output layer 2142, Looking at the residual plots it appears evident that all the models we trained so far are underestimating the price of low costs houses and overestimating the more expensive ones 34688, Merging with the shop and item datasets 148, Gradient Boosting Classifier 23837, Preparing the data for feature importance 31895, Creating submission 29894, Predict on the test set 12002, On applying grid search cv we get best value of alpha 1 28080, check the missing values again 24401, check the distribution of the mean values per columns in the train and test set 30677, Start with simple logistic regression 6035, Remove low features with low variances 40546, Export 1246, and plot how the features are correlated to each other and to SalePrice 24999, Performing some sanity checks before saving the dataframes to csv files 19518, Collecting the Data with Partitions 21894, Augmentation Settings 27932, I ll compile the model and get a print out describing it 4371, GrLivArea Above grade living area square feet 9789, Ordinal Values 33286, Title Encoder 35122, utils 8882, After diving into our dataset more another feature we can create is the Total Number of Porches by combining the following columns 525, As we know from the first Titanic kernel survival rate decreses with Pclass 24875, If you take a good look at names every name tells us 3 things 36549, Take Away 6363, Nice The most important feature is the new feature we created TotalArea 8651, The type of machine learning we be doing is called classification because when we make predictions we are classifying each passenger as survived or not 1735, Plot Age against Survived 34432, TF IDF 36353, Create the CNN Model Architecture 40255, Log Therapy to the rescue 31364, checking the files 20037, start by defining some constants and generate the input to hidden layer weights 3147, Cross Validation and Grid Search 980, Feature Importance 19403, there are 7613 disaster tweets and 21637 unique words 19459, Model Chart 22075, on average IF we have a prediction our prediction is significantly better than simply taking text 21474, We don t forget to save it 20128, App labels features 32032, Train a Binary Classifier with XGBoost 1833, Meaningful NaN Values 21737, Build the Model and Make Predictions 19514, Spark Core RDD 22808, Educational Level Primary 12260, J frac 1 2 sum i 2 frac lambda 2 sum sum W 2 19542, Data Visualization 30133, ConvLearner 5573, Model Bulding 12354, BsmtFinSF1 Type 1 finished square feet 32603, we can make predictions on the test data 7476, 3b Pclass 16241, The sixth classification method is MultilayerPerceptronClassifier This method is little different as here we have to provide it with layers also For eg here we give layers 8 5 4 2 where 8 demonstrate number of input features 5 and 4 are genral middle layers and 2 is number of output classes According to yor model you should define layers 33249, Missing values 27112, This doesn t help us much let s try to visualize the number of missing values in each feature 7379, Merging the unmatched passengers using surname codes and age 19358, There are 43 categorical columns with the following characteristics 19564, Dataset 23226, remove the duplicates and keep only the unique features and also we transpose the dataframe again into its original shape 34320, In case of train test split allowed inputs are lists numpy arrays scipy sparse matrices or pandas dataframes 5666, Create Name Length Category 2264, Embarked 41431, Item Category 2275, Fare per Person 7010, Here we first change the strings by integers because the variables are ordinal we can t get dummies unless the correlation with SalePrice is very low 20937, Close Session 29106, Glove 14438, go to top of section eda 32901, We ll try to use some tools to transform Chucky s image 31306, I experimented with parameters of compilation fit and network construction 320, SVM 23388, While a high learning rate at the beginning of a training run is great at the start it can cause issues as the model approaches convergence when smaller more careful steps are needed 33349, Calendar heatmap 25998, Upside Down with Spark 24733, Dimensionality Reduction 4013, Compare to sklearn implementation 33689, Import the library holidays to get holidays of almost all states 27919, Examine the Features once again 32529, Processing the Predictions 20714, As it contain many null values so we drop this column 34863, Pipelines 22224, lenecek g rsellerin bi imini ayarlama Setting the shape of images 24127, check how many missing values are there Location and Keyword columns 25801, let s focus on the training dataset and on the interest level of its top 100 contributors 13992, Extract Title from Name and convert to Numerical values 9833, Feature selection 19821, Weight of Evidence 38703, Before encoding 11668, Misc 11731, hope you find this kernel helpful and some UPVOTES font would be very much appreciated 25791, here we have only one feature which have some missing data 15499, we can use dummy encoding for these titles and drop the original names 31027, Phone Number font 41409, DAYS BIRTH 28360, analyzing the numerical features disturbion in previous application dataset 13024, Sex 43290, Um R negativo significa que nosso modelo pior do que um modelo que prev a m dia 14011, Acquire data 5542, Prepare the model 7927, Test basic models such as RandomForrest Lasso Regression 1686, SpiderMan How beautiful Just as we expected Sales Price increses with OverallQual Overall material and finish quality Shall we do this to analyse relationship with SalePrice for few more categorical columns 31233, Features with max value greater than 20 and min values less than 20 36603, Printing some samples which were classified wrong 30364, Test Washington prediction 35397, Modeling 16255, seq 10866, Fitting the Model 3444, The male title groups appear to be distinct possibly separable distributions 20722, Condition1 column 15262, Observation There are more number of people in the range 20 30 26039, We can print out the number of examples again to check our splits are correct 12283, Frequency bar chart 27199, 4th Step Data Cleaning and Editing 28947, try boosting 22229, Modeli derlemek Compile the model 18289, Tokenizing 19563, Preparing the model 22950, the last feature we have to tackle is the embarked feature 9774, Logistics Regression 4238, Grid Search 30709, define new augmentations 27513, Data check 33722, Data Formating 29831, Recurrent Neural Network LSTM 27560, In order to make this easier to use I ll output this as a CSV of the original feature and the deduced string 6697, Lets start with the bar plot between SibSp and Survived 34828, Important Error 14245, The survival rates for a women on the ship is around 75 while that for men in around 18 19 36834, Construct TensorFlow graph 20541, Random Forest Regressor 26444, To further evaluate our model we calculate the probabilities for all instance If the probability is the prediction for the instance passenger is died For probabilities the survival prediction is survived 24290, count the labels 32268, Relationship between variables with respective to date 16747, Survival by sex 25008, Mean Encoded Features 23818, Looking into each categorical feature 13225, GridSearchCV for Gradient Boosting Classifier 29785, Training 19809, When should you use k and when k 1 7282, Pclass Feature 31861, as well as the label vector of our training set 3842, Munging Fare 1357, we model using Support Vector Machines which are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis Given a set of training samples each marked as belonging to one or the other of two categories an SVM training algorithm builds a model that assigns new test samples to one category or the other making it a non probabilistic binary linear classifier Reference Wikipedia 15392, See which columns have missing values in training data 40279, Importing packages 35097, sample submission csv 34853, Undersampling 22463, Categorical plot 72, Dealing with Missing values 35525, Before model building part 6051, Exterior and materials 20701, How to Halt Training at the Right Time With Early Stopping 29962, WBF over TTA 5555, Optimizing Deep Learning Model 40273, Garage Type vs Sale Price 41225, There is no missing value and there is very little scope of feature engineering so we only do scaling of pixel values to bring them within and 10297, Data 5899, u Using Univariate Selection u 6666, Gaussian Naive Bayes Classification 18993, Define a neural network 15556, Tuning the parameters for SVC 14207, Train the final model 41677, Here s a look at some of the resized images of Benign Tumours 17268, Converting Categorial data to Numeric 2737, We can import the missingno library that can be used for graphical analysis of missing values and it is compatible with pandas 32067, Singular Value Decomposition SVD 2291, Submission 20796, Filling Categorical Missing Values 11561, now tune the best model using grid search Cross validation 29056, Histogram matching UPDATE 32535, Predictions 6792, Decision Tree 22653, With the best coefficients and intercept insert these into the sigmoid function to get the sigmoid values of the optimized coefficients 41841, Bi Gram 14658, If age is greater than 60 classify them as Senior Citizen 20029, Train with basic features 13086, Gradient Boosting 8574, Applying a Function to Groups 41018, These families are the Gibsons Klasens Peacocks van Billiards 4396, Handling Missing Data 11687, Building models with Your New Data Set 27014, EfficientNet 11501, Label Encoding some categorical variables that may contain information in their ordering set 15600, Survival by Age Number of Siblings and Gender 20836, walk through an example 21625, Style you df fast with hide index and set caption 14140, Support Vector Machines SVM 36080, Data Preparation and Feature Engineering 12236, Lists 12105, Experimenting with Random Forest 24263, Observations for Age graph 34918, Count other upper 16381, Importing different models 1636, Splitting train and test features 22026, now look at all 8 features together 19097, Creating Dummy Variables 40474, Linear Regression 26192, XGBOOST 9811, Matrix 2774, XG Regressor 31548, Electrical 11739, Once again we find mostly what we probably expected 22679, Predicting and Submission 20219, ReduceLROnPlateau reduces overfitting it simply reduces the learning rate by a factor of e half whenever there is no improvement in the monitored value here validation accuracy after three patience epochs 27390, Tuning feature fraction 42623, Checking for the optimal K in Kmeans Clustering 15143, Ticket 10448, MODELLING 11857, Creating Models 32208, Add the previous month s average item cnt 5486, create pair plot one more time to check outliers are coming or not 23050, Build CNN 16351, Create training validation testing sets 32665, One hot encoding is applied to categorical nominal features where there is no evident ranking nature among categories 16642, Logistic Regression 10373, Deviate from the normal 2901, Light GBM 33648, Feature Importance 4315, Combining Gender and Passenger Class PClass 23678, Sampling function 33734, Lets visualize some of the images with bounding box 38978, initialize bilstm 26185, PCA Principal Component Analysis 13115, LightGBM 3396, Feature Enginering 859, Uncomment if you want to check this submission 36219, Missing values left in the dataset LotFrontage GarageYrBuilt Electrical 17812, we fit the classifier with the train data prepared before 15445, Create a new feature called FamLabel which categorizes the family size 25641, Predict on test 36773, CORRECTLY CLASSIFIED IMAGES 39785, Logerrors vs Distance to nearest neighbors 38767, The train accuracy is 82 39276, SPLIT DATAFRAME DEPENDING ON SENIORITY 12664, Training and Evaluating the Classifier 33674, Periods 34393, This heatmap deserves a lot of comments 13979, Combine some of the classes and group all the rare classes into Others 24946, There are other ways that are also based on classical statistics learn org stable modules feature selection htmlunivariate feature selection 39220, Using Variance Threshold 28142, Chunk 1 28867, Attention Decoder 28333, Analysis based on Code Gender 41200, use a GradientBoostRegresssor model with parameters n estimators 4000 learning rate 0 23995, Get dummies converts Categorical data to numerical as models don t work with Text data 37564, FAIL PARTS 7733, Modeling 40873, Optimize XGB GB and LGB 27518, Model evaluation 42326, Previewing first fifty images 21224, We now work on predictions for test set 35445, Compiling 18509, plot a few more images at random 40299, the distribution is right skewed with upto 237 words in a question 27980, A large part of the data is unbalanced but how can we solve it 2353, Logistic Generalized Additive Model GAM 22786, Scatterplot 23723, Exploratory Data Analysis 39381, Proportion of null values within each feature 4588, We look at these features in addition to a few of their possible combinations 29836, Convert order data into format expected by the association rules function 22330, Removing Stopwords 6127, pretend there are no bath at these houses 10400, Create Submission Files 8940, Fixing MS values 12781, An example of an output of the Perceptron 13319, Pivot analysis div 19636, Duplicate devide ids 38930, for LotFrontage 7132, Fare 18579, Looks like the bigger passenger paid the more chances to survive he had 8684, Analyzing the Target e SalePrice 12169, Presort 15072, Family Size 31554, Random Forest 21380, Scientists created lots of network architectures coveryng lots of real world problem 2498, SibSip Discrete Feature 2718, Elastic net regularization 6168, SibSp Parch Family Size 24529, Total number of products per customer 31612, To evaluate the performance of a classifier model we can use the cross validation but the accuracy is generally not the preferred performance measure for classifiers especially when some classes are more frequent than others 25428, Excluding duplicates with clustering approach clustering check marking 29817, Vector Averaging With Fasttext 43088, check the new created features 35349, Grid Search Hyperparameter Tuning 32867, Item sales count trend 32991, Report 18105, We can optimize the validation score by doing a over rounding thresholds instead of doing normal rounding 6649, Extract Initials from the Name feature Categorize the Initials by different values 40867, Optimizing Hyperparameters 21675, Data builder function 29461, Cross Validation in Neural Netword 12395, Retraining the model over the whole dataset 5292, Being inspired by other Kernels and this blog it is necessary to transform the numerical features that are skewed 20305, Fresh fruits and fresh vegetables are the best selling goods 38214, Data Augmentation to increate training size 43001, function below go through each column one by one to do the describe it is really good pratic but it takes long time to process 21595, Print current version of pandas and it s dependencies 21334, Chuy n d li u t ch th nh s 5373, Recursive Feature Elimination Cross Validation RFECV 42163, The BsmtFullBath FullBath BsmtHalfBath can be combined for a TotalBath similar to TotalSF 15182, Confusion Matrix 2144, A few noticeable things are 26745, Plotting sales of weekends preceding each event 25775, female is more likely to survive 11212, XGBoost 28170, Features 7495, Choose the features that be used 11249, Feature Transformations 33571, Apply Cox Box transformation and create cleaned train test data 6625, Exploratory Data Analysis 14113, center SwarmPlot center 7988, Dropping 32234, Here is our optimizer 21130, For all categorical variables which don t belong to class luxurious we apply NA correction by imputing level Unknown 24013, Training function 34005, weather 6341, Number of missing values per column 24957, sex in Train Test set 32311, Relation between Survival and Passenger Sex 2139, Error Analysis interpretation and model improvement 13209, let s loading the required librarys to the decision tree plot 27498, Shallow CNN 2 with regularization 9620, Feature Scalling 20953, Another way of verifying the network is by calling the plot model method as follows 28813, Plotting out Loss and Accuracy Charts 25903, Threshold value search for better score 3608, Data Correlation 23406, Transformed to numpy arrays 29146, Feature importance via Gradient Boosting model 8427, Group by GarageType 28576, BsmtUnfSF 32989, Definition 19801, Mode 4120, Check different values of lamda Usually follows tukey s transformation Lamda as all the features are positively skewed we can go for lambda or The output generated as gave the lowest RMSE score after different regressions was applied on the dataset 11837, tackle all the missing categorical test data 17648, Decision Tree 29149, Transform necessary features to object type 37735, Numeric Column Optimization using Subtypes 16487, we can check out that we re missing some age information and we are missing a lot of Cabin information 29823, Trained Glove 24557, Distribution of products by age group 30974, we define the random search function 5654, Create Name Length Category 14632, From the violon plot of Age and Survival along with the KDE plot that was constructed during the bivariate analysis we can conclude that there were certain age categories that fared worse 1533, Cabin Feature 2540, go to prepare our first submission Data Preprocessed checked Outlier excluded checked Model built checked step Used our model to make the predictions with the data set Test 19955, The Cabin feature column contains 292 values and 1007 missing values 12298, Surname 28562, Interior 27251, Scale the data 27630, Properties that have been sold more than once have multiple transaction entries 1214, Concatenate train and test 14845, Embarked 39148, also look at one image in more detail 8038, Age 15733, Precision and Recall 40629, we can save the predictions to disk and zip them ready for submission 10864, Checking the skewness of other variables and treating it 32644, Clustering 21457, GBR 41952, Visualization 23748, Before submitting run a check to make sure your test preds have the right format 22325, Spelling Correction 42445, We have 59 variables and 595 19802, Random Sample Imputation 21942, Spark 11802, Again using log transformation to remove skewness as all of the numerical features have positive skew 14341, Try Knn K Nearest neighbors 6063, Fireplaces consider turning it into binary 29790, Model 39420, check if there are any NaN values left 2000, It makes sense that people would pay for the more living area 37944, Feature Engineering 1316, checking for missing and unique values in combined dataset 10219, Majority of passengers embarked from Southampton it may be the journey start point 15115, Modeling 17840, plot the parameters for the classifier 4239, Automated Hyperparameter Tuning 15442, Creating Title feature from Name feature 35919, We plot 25 random test images with their class using list of predicted labels 13078, we set our PassengerId as our index 18883, There are couple of outliers here we can procede bothways 33855, Distribution of the token sort ratio 5740, Installing the packages 7945, we need to specify 17589, Grid SearchCV 15082, XGBoost 21565, How to avoid Unnamed 0 columns 38698, After Encoding 1825, Regularization Models 1574, The Ticket column is used to create two new columns Ticket Lett which indicates the first letter of each ticket and Ticket Len which indicates the length of the Ticket field 37893, Evaluation 42028, Split and join to have a new str order 22494, To measure how well we normalize addresses we check the unique number of addresses before normalization and after as a last step of cleansing we remove all characters like and and then strip all space symbols 31631, Dates 23524, calculate some statistic for each class 28123, Disaster tweets are Less in Number as compared to Non Disaster Tweets 30928, False Positive with biggest error Sincere but predicted strongly as insincere 9151, If there is no Kitchen Quality value then set it to the average kitchen quality value 32668, Segregating train and test features 37649, Create folders for the train set and validation set I created two different cats and dogs folder both in train and val Because I am going to use ImageDataGenerator the flow from directory method identify classes automatically from the folder name 23994, we have 86 columns having added around 7 more to our data 4010, compare with the sklearn implementation 6657, Chisquare Test for Feature Selection 18372, Taking Care of Auto Correlation 9295, Regression on survive on Sex using a Categorical Variable span 1535, Embarked Feature 6224, MasVnrType Masonry veneer type 11555, Engineering rare categories 1032, news Most of the features are clean from missing values 24242, Descriptive analysis univariate 557, submission for knn 29460, NeuralNetword Model 6448, Linear Regression 42210, Splitting data 6910, Fare 10927, Complete graph with 100 people 34467, Trends 35794, Find best cutoff and adjustment at high end 20247, Apply the functions 41155, FEATURE 1 NUMBER OF PAST LOANS PER CUSTOMER 37688, Ok now you know the rules is black is white and is gray 30692, Voltando ao c digo 38070, Trigrams Analysis 9082, There are only 2 rows that have the Conditions in an order that is flipped but given that most of the time the conditions are equal I was to perform feature engineering to build a relationship between these 2 seperate columns 21156, We improved it just a bit 1054, We use RobustScaler to scale our data because it s powerful against outliers we already detected some but there must be some other outliers out there I try to find them in future versions of the kernel 3785, Plot learning curves 36843, Evaluation 29858, Understanding dicom dataset structure 30358, Predict by Specify Country 2158, We don t need PassengerId for prediction purposes so we exclude it 31064, Percentage 31274, Rolling Average Price vs Time WI 14784, Fare 7058, The tuning parameters were obtained from GridSearchC 31541, Some categorical columns have big impact on neighborhood data some should be filled by NA cause lack of subject 40417, Display Address 28752, load the training data and validation data 17854, Submission model with hyperparameters optimization 1544, Splitting the Training Data 30412, Main part load train pred and blend 13735, Creating output file 28371, Text Cleaning 29898, One Hot encoding of labels 10081, Data Preparation 11831, Percent of Missing Values 2312, Sklearn Label Encoding Method multiple int options but 1 column right table in pic 20718, here we do label encoding instead of one hot encoding 37318, Perform rotation on the picture move up and down left and right and other operations to increase the amount of data Data augmentation is very effective Parameters such as rotation range zoom range width shift range and height shift range can be modified The following parameters are the version used by most people but you can also explore parameters that are more effective for your own model 27551, Display interactive filter based on click over legend 17008, Missing values Age 24562, Distribution of products by sex 13984, Impute Embarked with it s majority class 7994, If all classes of a category was false we delete it 16852, Dropping some variables 33470, Sales 7646, deal with missing data 18640, ANTIGUEDAD 32053, High Correlation Filter 18229, Test Gradient Boosting 32554, Merge other cases 2654, Can you think of any way we can use name column 9522, Random Forest 22443, Diverging bars 22175, We have features column which is a list of string values 40649, Concatinating all the features standardscalar tfidfW2v 41998, Sorting columns looking at certain rows 21800, Model 1 Baseline LGB 1656, Same story with previous feature categorical with 7 categories Actually SibSp and Parch are very much related but let s leave this for tutorial for beginners part 1 17534, Extract first letter from Cabin property and transform it to numerical categorical feature 21194, L model forward 38172, The Evaluate Returns 16025, Sex 6839, Feature Selection 36960, Feature Importance 26814, Class Activation Maps 42185, True Positives TP True Negatives TN False Positives FP and False Negatives FN are the four different possible outcomes of a single prediction for a two class case with classes 1 positive and 0 negative 11257, The initial learners now be used to make predictions on the validation data and to make predictions on the test data 2273, Fare 29225, For Testing Data Set 37035, Can we get some informations out of the item description 35925, Make prediction on the test data 3627, Features Year Built Year Remodeled 3285, drop these outliers 10398, Final Prediction 4082, Scatter plots between SalePrice and correlated variables move like Jagger style 8894, LASSO 36881, Logistic Regression 36265, look at Number of siblings spouses aboard 35437, Lets Train our Model 41115, Transaction Date Vs Mean Error in each County 25680, India Cases vs Recoverd vs Deseased 9416, Train test split 17804, And let s plot the Age clusters grouped by the survived and not survived 22021, var36 26968, which item name is the most popular and the worst 15060, Most Positive Correlation 5910, u Gradient Boost u 9633, Saving IDs column 19194, Predicting using the XgBoost Regressor with Hyperparameters Tuning to give the best predictions 619, The next idea is to define new features based on the existing ones that allow for a split into survived not survived with higher confidence than the existing features 35059, Complexity graph of Solution 2 2980, Data Visualization 25458, predict the test set using augmented images 29634, with our voting classifier we try to predict whether passenger in test set survived the catastrophe or not 26617, Read the data 11504, we don t need scaling because of the we did log 19350, can log price so that decrease large gap 7457, Creating new Features 36410, Basic Pre EDA 4059, Enhancing the Missing Age Solution 37868, Distribution of Categorical Features with respect to Sales Price 20777, we can implement the Doc2Vec model as follows 42007, Sorting and Counting sorted values 19709, Since train data is a list let us convert it into a numpy array 3015, We now start the process of preparing our features we first find the percentage of missing data in each column and we determine whether the threshold of missing values is acceptable or not 38410, Model selection 9392, K fold preparation 36479, Private dataset and EDA 27845, Plot errors 9401, This be the final set of parameters I use here for my prediction 16492, Creating Train Test Split 11477, MiscFeature 6017, Bisa dilihat algoritma Random Forest kita tidak kalah jauh kok mesin merekomendasikan XGBRegressor karena tidak terlalu overfit jika dibandingkan dua algoritma setelahnya 18991, Represent train test texts with token identifiers 24509, create our DataBunch 33272, CutMix data augmentation 16693, We can also look at the survival rates 6029, Detect and Remove outliers 18014, Survived 12020, LGBM 39152, it s time to train the neural network 30895, hardcoding the locations of the main cities in CA 20416, Feature word Common 3875, Does building a garage after few years make the house more valuable 17943, preprocess test data also 33336, Fix shops df and generate some features 34908, Make mean target encoding for categorical feature 39122, XGBoost 39112, SibSp 32911, This is the augmentation configuration we use for training 24435, Filter 15906, Findings 7818, Architecture 35528, Multiple regression models combine with an ensembling technique 14769, try another method a decision tree 4001, The obtained parameters are very far from those that we obtained analytically 9786, that problematic values dropped we can work on filling missing values that lie below the threshold 1810, Fixing Skewness 32821, Starting importance variables evaluation 9426, BoxPlot 3185, With both models I need a scaler 24750, Converting variables 1134, Feature ranking with recursive feature elimination and cross validation 42245, Data Cleaning Preprocessing 13400, Plot the classifier accuracy scores 13058, Voting Classifier 34486, Train the model 18016, Survived by Gender Pclass 17353, we model using Support Vector Machines which are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis Given a set of training samples each marked as belonging to one or the other of two categories an SVM training algorithm builds a model that assigns new test samples to one category or the other making it a non probabilistic binary linear classifier Reference Wikipedia 22221, Veri setinden rnek bir g rselle tirme Vizualization in dataset digit image 6084, Classifier Comparison 36811, Then as always we tokenize the sentence and follow up with parts of speech tagging 24920, AUTO Correlation 31091, BsmtFinSF2 font 9683, Importing libraries 33588, Data Augumentation 8110, Parch SibSp 15255, Fill Null Values for Age Feature in train and test dataset 18476, Since we have no information whatsoever on those missing values and no accurate way of filling those values 29024, And some nice scatterplots 29181, One Hot Encoding using pd getdummies 8289, Stacking At this point we basically trained and predicted each model so we can combine its predictions into a final predictions variable for submission 6664, Linear SVC 36109, Correlation of each feature with our target variable 31775, Predictions class distribution 25936, Missing Values 1781, remember our feature scaling earlier on train test split 12135, Basic evaluation 2 36557, Take Away 20634, lets consider only those words which have appeared more than once in the corpus 7146, Deployment 16135, Total rows and columns 32531, Model Refining Model 39232, WBF over TTA 904, SalePrice correlation matrix 31786, Calculating the first metrics without Bayesian Optimization 13742, Confusion matrix and accuracy 26401, It is remarkable that the percentage of men in the third class is much bigger 3437, Some titles like Dr and Rev we ll create specific indicator variables for 41158, FEATURE 4 OF ACTIVE LOANS FROM BUREAU DATA 42766, Age group 14187, Creating dummy variables 27999, Spliting Test and Train Data 12205, As we wanted the data flew through the pipeline getting cleaned transformed and rescaled 30954, Appendix 6064, FireplaceQu missing values missing fireplaces sparse classes 1194, drop these from X and testing features 39957, ENSEMBLE METHODS 22493, Bonus9 Animation in matplotlib 39857, Creating the Model 42777, Confirm that the shape is what we expect 42k in train 28k in test with 784 pixels per row 7714, Data Visualization 37022, Top 15 second levels categories with highest prices 15376, The code can be read from right to left and is a list comprehension and is executed as follows 25915, MODEL BUILDING 23291, Correlation Coefficents 3775, Data Wrangling II 2773, Modelling 32242, we can run the training 11854, Reverse log transform on SalePrice 8875, Merging finalTrain and finalTest 16966, Encode categorical feature 7624, booster gbtree gblinear or dart default gbtree 18771, More filtering of data to try 19710, try visualising a photo from the converted training set 42893, I thought of a sigmoidal function because China s data resembled a sigmoidal shape 42153, check how the Overall Quality rating affects the median price of the house 12922, check the percentage of survival for males and females separately 29423, We use a multinomial Naive Ba model for this notebook You can go ahead and choose your own model as per you like can also play with this model s parameters so as to increase it s accuracy But for me this gave a accuracy of around 79 3607, Outliers 28030, CLASSIFIERS ACCURACY COMPARISON ON OUR WORD2VEC MODEL 11735, In the correlation heat map we get some important information 28529, OpenPorchSF 26206, I assign a class 1 which is the label wheat 9073, Feature Engineering 11690, Random Forest Classifier 27028, use Z test 21583, Select data by label and position chained iloc and loc 27482, The model needs to be compiled before training can start 17383, Training Our linear SVM 31304, PCA decomposition can be inverted 17037, parameters gamma 42805, Model evaluation 29832, Recurrent Neural Network GRU 11063, Feature Engineering 33827, Missing values Treatment Techniques 12070, 1stFlrSF 36386, SGDRegressor is sensitive to scaling and normalization 28751, Dataset Loader function 12407, GrLivArea 33830, Suppose we want to keep only the rows with at least 4 non na values 17995, Instead of having seperate features for the number of parent children and sibling spouse we can use the family size as a single feature for accompanying family members 18521, We can get a better sense for one of these examples by visualising the image and looking at the label 42015, Creating a new Dataframe with a certain column 260, Library and Data 20212, Plot the data 42817, Preparing the Data 28245, Lets check the missing values in each file 17456, switch back survived value 35845, Lets create prediciton on DT Model 25224, Data Preprocessesing 22801, Data Cleansing 3962, Data Preparation 40251, Garage Area 26284, Defining the neural network structure 7612, Loop over Pipelines Ensembles 8330, organize the data a bit better 9903, Modeling 6074, SibSp and Parch 8590, Deleting Cabin feature 8750, Validate Model 23346, Learning Curv 33514, Albania 19144, Model 4 Input ReLu 512 Dropout ReLu 128 Dropout Sigmoid output 19724, Observation 10737, Bidirectional LSTM GRU Attention 21562, Fare 36569, Save images 17736, The letter in each cabin number refers to the deck the cabin was on 17716, center Final submission center 34031, Scaling 12464, Model Fitting 15316, Checking out the distribution of Fares 28520, YearBuilt 2557, Encoding the variables 34648, USD RUB exchange rate taken from 24143, Voting Classifier Model 15119, Looks like the Random Forrest Classifier got us a little higher score of 84 given the paramters we fed into the model 26414, let s go back to the histograms and check how the distributions look like for FareCorr 7782, value counts with relative frequencies of the unique values 17415, Plot final XGBoost Decision Tree 9525, Library and Data 24364, This looks much better than the previous one 18331, Looking at SalePrice distribution 14536, Feature Importance 13025, Age 25879, Trigram plots 41047, Order By Timing 13206, First let s create a basic decistion tree model using all information that we have 7558, we got our best algorithm with accuracy of Gradient Boosting 37280, Our standard embedding matrix builder 28601, HouseStyle 4692, Numerical values 11911, Lets focus on the Missing Values 26720, look at the unique days at which the 10 SNAP days of a month exists over the years 28700, Stacking 15121, Before handling Age null data I m gonna separate few class with 10 years 42804, Log of our data have distribution close to normal with exception to two abnormal peaks on left side 6154, Fit the basic models 13192, First let s deal with name variable here i extract the name title of each passenger passengers that have not a title i replace ad unknow latter i ll do one hot encoding in this new variables and another categorical variables 7967, Wrangle data 9889, we try to write a code to complete missing age variables 27041, Location of imaged site w r t gender 38759, The train accuracy is 82 27948, Drop columns with categorical data 36856, setting train and validation data 2024, XGBoost 21473, we just have to apply it to the whole dataframe We don t forget to shuffle it afterwards otherwise the batches all have nearly identical rows 7051, Masonry veneer type 31239, As per the Kolmogorov Smirnov test 46 features have a high probability of not being from the same sampling distribution 14477, Most of the youth male died siblings survived both only i can say that high class passengers in male might got save 27933, Learning Rate Decay If the models loss does not reduce for 2 consecutive epochs reduce the learning rate This should stop the model stepping over the optimum too many times 23488, Hashing Vectorizer 25372, Label encoding 3939, Bivariate Analysis 30141, Training 33457, StateHoliday 25492, Drop columns with categorical data 9226, Execution on Testset with Random Forest 21118, Define train and test data sets 7806, Iteration 3 Setup with Advance Preprocessing 11517, Grid search for RF 36553, Rounding quantile based binning 32939, Load the data 4473, Dealing with Fare Missing values 8474, Residuals Plots 19865, we have got around 144 outliers 15175, Missing values part 2 18193, There are interesting things 30657, Bags have been transformed into Bag 18613, Model 2 SVM Linear Accuracy 79 27764, SUBMISSION 39851, Seasonal ARIMA Model 6225, that s it there no missing values 3727, There are three ports C Cherbourg Q Queenstown S Southampton 16603, Discrete Variables 39384, The idea is to replace the null values of renta with appropriate median values 9412, Fancy imputation font 31743, Torchvision transforms 27529, Display heatmap of quantitative variables with a numerical variable 15579, TicketGroupSurvivors 25170, Replacing common words like 1000 to 1k or 1m and many other and removing special characters 19351, Second try 7899, I explore the entropy to check wheter the values can give a good learning to the algoritmh 3246, Before moving further let s take a look at missing values in Numberical and Categorical columns 14196, Creating Model 3504, Separate into inputs and outputs 36702, Accuracy of the model 20679, While fitting the model a progress bar summarize the status of each epoch and the overall training process 32125, How to replace all missing values with 0 in a numpy array 40008, Overlapping patients in train test 15736, After filling AGE values we can focus CABIN 11167, Transforming some numerical variables that are really categorical 7295, Comparison of All Models 33288, Age Grouper 18230, Test RNN Model 7668, Missing values percentage per column with less than 80 12088, Naive Bayes classifier 5210, predict the output and making an output CSV 1318, data after tuning 3435, Exploring the Title variable imputing missing Age values 5331, Visualize three way split of three variables 29391, now check our final CNN model 14568, We can combine SibSp and Parch into one Family which indicates the total number of family members on board for each member 27273, Infact our data is not normal distributed we can achive better score with Gaussian constran removed 28274, Fine tuning the model by finding the best values for the hyperparameters using GridSearchCV 37164, We use nn 15332, Searching for the titles and extracting them from the names in the given data 4074, Bonus Plotting Feature Importances 36725, Define validate function 4044, we can check the general attributes of our data and the non null elements 42401, Weekly cycle decompose of CA 1 store 42470, Selecting features with a Random Forest and SelectFromModel 36849, some global variables 2190, Top 50 Corralation train attributes with sale price 12476, Embarked 37081, Correlation among Base Models Predictions How base models predictions are correlated If base models predictions are weakly correlated with each other the ensemble likely to perform better On the other hand for a strong correlation of predictions among the base models the ensemble unlikely to perform better To sumarize diversity of predictions among the base models is inversely proportional to the ensemble accuracy make prediction for the test set 18813, Import libraries 37556, Get file structure 39064, Paragram Embeddings 38157, We already have our train and test sets so we just need to choose our response variable as well as the predictors We do the same thing that we did for the first tutorial 35623, Some necessary functions 28022, CLASSIFIERS ACCURACY COMPARISON ON COUNT VECTORIZER 42147, Decoder 24564, let s look at the total number of products by age income and seniority 21241, The Generator Model 29971, Splitting train dataset into subtrain and subtest Training data with LSTM model 6757, Checking Skewness for feature MiscVal 20329, Create a submission file 40137, Create our class instance 6942, Here I bring categories into a numerical format 43357, Target 20044, look at the train data file 5928, Don t know how to find outliers 39499, Got the idea about its 3618, And then this happens 10439, The missing data shall be removed now 3877, A Transformer by definition should support the fit transform interface There are two ways to achieve this 39669, Input tensor placeholder X needs arbitrary amount of 2D inputs 40097, spaCy s CNN 3500, Make predictions on the test set and write them to csv 31665, Evaluation of model with 3 classifiers 20594, Exploring Missing Values 17004, Explore and Engineer Features 34177, Interpreting ANN model 1 10205, Train Test Splt 9804, Stack Model 3 Feature Disintegration with PCA 14156, First we plot the distributions to find out if they are Gaussian or skewed 37700, just like numpy right 18195, there are products that were never returned 19983, Attention for text classification 4793, Since the SubClass are categories and not of numeric data type we covert the feature to category type 36055, Create new features 35522, Another part is checking the distribution of Sale Price 12433, Feature Engineering 28366, Independent Variables 26671, Income Type 18532, Here we have the first rows of the train and the test dataset 1159, LightGBM 16994, Download data and preparing to prediction including FE 14218, The Chart confirms a person aboarded with more than 2 siblings or spouse more likely survived 16928, Analyze data 14306, As we only cascade the test and train data for applying the feature engeering We donot mix the train and test data cascading help in apply feature in single run of code not applying seperately for test and train And we able to use train and test data sepreatly 37108, Code in python 2141, If we compute the residuals and plot them the pattern looks even more evident 26453, The validation score evulated by cross validation is quite close to the 83 1983, Converting Categorical Features 6453, Creating Submission file 29379, Begin Training 26219, Vertical Flip 20603, Since most of the values are in Mr Miss Mrs we can include the others in separate category and hence have four categories 15664, Reforcast predictions based on best performing model 17427, How many values are missing 9936, Combining the dataset p 9842, Logistic Regression Model 8565, Deleting a Column 29995, LinearRegression 36826, Linear Classifier 25259, Split DataSet 3570, GarageArea is divided into zero and non zero parts 20809, Split X and y into train and valid data for model testing 35173, Plot the model s performance 24770, Rank model performance 18238, Softmax activation prediction and the loss function 29185, Visualizing 4618, Lets have a look at the Dimensions of the data 30321, This notebook deal with positive negative and neutral samples independently 25292, These are the probabilities that the image is any of these numbers But we don t want that We only want the highest probability 37653, Train 24864, First we notice that the Survived category not have any consequence if we drop the Name category 12174, Custom Build Submission Function 10032, CORELATION PLOT 29052, Adaptive Histogram Equalization 6681, Confusion matrix for the best model 1410, check how the distribution of survival variable depending on the title 40472, Training 37703, It took 12 seconds and we used the whole dataset 7750, We need to know how much each feature is useful for us to predict prices which means it should be corrolated with SalePrice 14646, Run encoding on the combined train test data 24108, Extracting Models 34023, Count Hour 34321, Make sure that the labels are proportionately equal before and after the train test split 38, PassengerId 62 and 830 have missing embarked values 28686, YrSold 42206, Checking Error 6482, Imputting missing values 42269, bedrooms bathrooms Category Category Values 33766, Distribution of Labels 872, For females with Parch and Pclass survival rate is below 7148, MANIPULATING DATA FRAMES WITH PANDAS 8311, Checking for features with missing values 5913, RandmoizedSearchCV vs GridSearchCV 6827, Target Distribution 26815, Submitted Test Set 20720, LandSlope column 10669, Hierarchical Clustering 15233, Splitting training data and testing data 15272, Random Forest Algorithm 1101, Variable selection 37731, Feature Selection 34529, Create Custom Feature Primitives 16355, Comparison of base models 38144, Is ensemble default in auto sklearn 43318, KNN is the best classifier for the dataset 41566, Stage 5 Understanding and Applying t SNE 5831, we can deal with these missing values in 2 step identifing categorical and numerical feature separately 11693, The accuracy of KNN classifier as reported by Kaggle is 77 12637, Filling in Missing Values 3019, Looking at the distribution of the numeric features 7019, Evaluates the general condition of the basement 42462, ps reg 02 and ps reg 03 11235, go to the prediction part 28020, MLP 1127, Considering the survival rate of passengers under 16 I ll also include another categorical variable in my dataset Minor 29967, Visualizing dataset 22102, Predict Labels Targets for Test Images 10654, Compare Model 25185, Importing Libraries 39497, Function to explore the numeric data 15441, We find correlations between features and impute missing values using the correlations 21596, Check if 2 series are similar 15840, PClass 27130, Our target feature is a continuous variable with values ranging from 34900 to 755000 13345, Modeling div 19536, you can check the lineage 10383, Fixing missing data in test set 13883, Passenger s Gender 8817, Filling missing values in Fare Cabin and Embarked 28204, Setup 5098, This is a list of new features 11224, Show new predictions 36113, Seperate numerical and categorical features 23896, There are no visible pockets as such with respect to latitude or longitude atleast with the naked eye 1185, Incomplete cases 4402, Hyperparameter Tuning 9906, I am going to compare 5 different Machine Learning models which are 20699, How to Get Better Model Performance 40661, The next two be tree models 7327, Create a feature Names to store the length of words in name 4155, Missing Value Indicator on Titanic dataset 1277, Majority of passengers borded from Southampton 16480, Survived Plots 3862, examine the attributes 36574, visualize the distribution of less popular apps 12913, View concise summary of test set 31083, Selcting Union of Features from both the ways 36348, Train the Model 10441, Normality 36555, Gaussian Mixture Clustering 979, Confusion Matrix 263, Compiling model 28729, The most expensive products 35081, implementing the scalar over the x train and x test and transforming them to x train scaler and x test scaler 31739, Ben Graham greyscale Gaussian Blur 20450, installments payments 201, Model with plots and accuracy 5448, the Class version Lets turn it into a class with a single function still only doing 1 feature 30852, Location for Non Criminal Activity 39668, Normalize input incase image array numbers in 0 255 range 31378, Flip Image LR Up Down Transpose 38981, Location encoding based on 9030, There is 1 row in the testing dataframe with no value for KitchenQual 27374, Adding lags 24272, Naive Bayes classifier 13048, Embarked 31773, Loss 7396, Import Data 38313, Final Prediction 29848, Label Encoding some categorical variables that may contain information in their ordering set 40446, Street and Alley 10826, As always let s make a prediction 6834, Outliers 37566, majority of the columns are integers with 8 categorical columns and 1 float column 20406, Reading data and basic stats 33307, Feature Importances 36354, Train the Model 10832, Before we start tweaking the classifier I would like to check one thing 28943, PyCaret Library 6182, The number of siblings present for passengers 24461, only cutout with 8 holes 25429, Simple EDA 4570, Third step Missing data 2266, Fare 18387, Algorithm for finding selected text 7666, Features with 80 missing values we drop 10777, Voting ensembling 32890, Trained on validation set using the 1st level models predictions as features 23839, Taking X122 and X128 18930, Relationship between numerical values exposing most data points by color gradient 13535, Gradient Boosting Classifier with HyperOpt tuning from my kernel Titanic Comparison popular models comparison popular models 35237, Already we analysed punctuations of selected text now lets have a peek into whole text 638, Sharing a ticket appears to be good for survival 31601, MODEL BUILDING AND PREDICTION 2752, We are going to use the House prices Advanced regression dataset to point out a mistake that poeple might make while handling missing values 1559, Age 1331, Correlating categorical and numerical features 13718, Plot to check distribution 20438, application train 42724, With lmplot from seaborn we can draw linear regression plot very easily 23455, Year 13157, Custom Mappings 2990, Create dtype lists 36698, Seperate Train Set 22998, Standard deviation analysis in each store 32267, Relationship between variables with respective to time Represent in stacked fill line 20849, To run on the full dataset use this instead 39698, Lemmatization 36358, Training our model 8995, For LotFrontage I am confused why some of these values would be null 22353, Writing the id and loss values to a csv file last line of which contains the MAE value 29437, Null Values and unuseful data 16638, EDA 9421, Count Plot 29375, Folders to store the images in format that allow to run fastai algorithms 5434, Many missing values are they all without a pool 5520, Ensembling 31246, strong Sex strong 30678, 3775148606625 21420, Outliers 19359, Exploring numerical columns 33477, In order to solve the differential equations system we develop a 4rth order method 6207, Linear SVC 29128, investigate for errors Curious let s dive in 14706, GETTING MODEL READY 19301, Data Interaction 41989, Locating loc To read a certain row 21902, Summary 23324, Number of month from last sale of item Use info from past 27053, Extracting DIOCOM files information in a dataframe 14521, Observations 22509, start session for prediction 17974, Embarked 18213, Primeiro formatamos os dados para em vez de ser uma matriz 28x28 ser um vetor de 784 valores 27525, Data 19551, Reshape 30720, Data Augmentation 34086, Hyperparameter Tuning Grid Search Cross Validation 8256, Creating a Dataframe listing every feature with missing values 25235, We had log transformed the Ytrain and hence it is essential to transform it back to original by taking an exponential of model predictions 5307, Selecting meaningful features 2806, AutoViz 20205, impute mode value to categorical features 32307, We have 177 NaN in Age and 2 in Embarked 8473, Modeling 8086, Blend models and get predictions 27058, Define a model with best tree size 34472, Plotting feature importances 18698, Accuracy 32129, How to do probabilistic sampling in numpy 8680, CATEGORICAL FEATURES 5803, Data for the exercise 29028, Feature Engineering based on this excellent notebook 1580, Having built our helper functions we can now execute them in order to build our dataset that be used in the model a 28616, RoofMatl 27878, the properties of the categories should be similar 8415, Reviwe Porch Features 42457, Checking the cardinality of the categorical variables 39119, Gaussian Naive Bayes 22604, Months since the last sale for each shop item pair and for item only 10222, start Feature Engineering with creating new variable Family Size by adding SibSp Parch and One Current Passenger 6261, It looks as though passengers having many siblings or spouses aboard had a lower chance of survival Perhaps this ties up with the reasoning why passengers with a large value for Parch had a lower chance of survival 10680, HERE SalePrice is mostly related to 17745, examine fare NaN s now 39672, Create input X and output Y tensor placeholders that correspond to 2 coordinate inputs for X and 3 RGB color outputs for Y 19696, Compile Model 27976, distplot 6405, Great There are no negative values in the dataset for sale price which is good 24229, We be using the Sequential model from Keras to form the Neural Network Sequential Model is used to construct simple models with linear stack of layers 12523, Combining train and test data and separating the label 10734, XGBoost 34936, Stack it 23441, Checking for null values 3264, The target variable is right skewed 27900, Simple training only 10 epochs just test it 4564, Defining features and target variable 41967, Distribution Checking 31632, Dates Hour Dates minute 18468, Deep Dive on Stores Closed on Certain days 19434, Take a quick look at correlation matrix as a heatmap float 32237, Here is the accuracy of our model for the validation set 14563, Embarked Embarked is a categorical variable here we can impute the missing values with the most popular category 12955, Detecting missing values 28433, Test Set 10641, Encoding Categorical Variable code Title code 8865, Outlier Analysis 15188, Finding Missing Values in Embarked 4002, RMSE is also very close to sklearn and far from our analytical solution 10843, check missing values 15659, Ridge Classifier 3400, More Features Engineering 41164, FEATURE 10 AVERAGE NUMBER OF LOANS PROLONGED 12640, Gender Feature 20170, Training Data Size Vs Accuracy Fitting Score Times 28817, No surprise in Decembers the stores have more customers and make more sales 19967, Prediction 18606, We need a that points to the dataset 39211, One of the samples is listed with 112 bathrooms 24570, Observations 42253, Cross validation 25344, Data Augmentation 37170, a random forest 26497, For most classification problems one hot vectors are used 25219, TotalBsmtSF Total Basement Square Feet 2311, Words Labels as Features 20941, RESULT 18763, load itk Used to read a CT Scan for the mhd file 11882, Exploratory Data Analysis 28453, Analyzing column of type int parcelid 8719, MICE 26523, Model and Model Evaluation 18389, Recalculate word weights using the entire training set 13694, We convert the Cabin data into a flag about whether a passenger had an assigned cabin or not 32248, Size Matters 7366, have a look at the table for the 1st class 1165, NA s 42834, Training and Evaluating the Model 2102, Another way of looking at these features is to combine them in a plot together with another numerical feature 41682, Since the data is imbalanced meaning there are a lot of records for benign tumours while very less records for malign tumour 29822, Trained CBOW 9839, Linear Support Vector Machine 6033, Remove correlated features 1216, String Values 26798, Visualizations 7874, The next option is to cerate IsAlone feature to check wheter a person traveling alolne is more likely to survived or died 34049, Dummy Encoding of PdDistrict 17263, Ticket Feature 2831, concatenate the saved Id with the predicted values to create a csv file for submussion 36997, Missing Data 42144, We re trying to build a generative model here not just a fuzzy data structure that can memorize images 38311, Decision tree 8720, KNN Standardized Features 3938, Univariate Analysis 13566, use some features to help us fill Age NaN s 20696, How to Plot Model Learning Curves 21922, LIghtGBM Regressor 14388, Extract a title for each Name in the train and test datasets 30620, Less than 40 of passengers in the training dataset survived p 7917, Select the numeric and categoricals columns 19890, Expanding Window 7281, Title Feature 8797, Deleting Outliers 9934, To handle the missing values in the age column of the dataset I have calculated the average age of the males and the females in the dataset and replaced the missing values accoring to their sex p 19541, Make Predictions 5443, On to the real Data 12972, We can say 10833, make a quick summary 7775, Extra Tree 17457, Ticket class 35405, Applying Random Foresting 25424, Idea to try predict the mean of the target using the date 7550, SVM 10467, let s create a merged plot of the top 6 strong correlated features with the target 9439, Zoomed Heatmap 17397, Age wise Survival probability 27001, Data for the network 1966, LogisticRegression 9230, Train Model 75 15 29055, Histogram Normalization 5151, we need to transform the strings of these varibales into numbers 36860, first 10 image samples for each digit 19580, Matrix shop item 33234, Transfer Learning Example 11771, Analysis and Visualization of Numeric and Categorical Variables 17354, In pattern recognition the k Nearest Neighbors algorithm is a non parametric method used for classification and regression 14694, Categorical Features 34713, Mean over fixed subcategory and month 18345, Remodling of house 7481, 4a Titles 31106, Label Encoding one Column with Sklearn 10033, Missing value is data set 10881, Examine the Distribution of the Survived Column 25328, The Porto Competition 29990, The following dataset is modified to work with our hdf5 file 171, Now we can make predictions for our test set Notice that we quickly transform the Sex values to numeric ones dummies by using the get dummies pandas method in order to bring them to a suitable format for our Logistic Regression model 1743, Since fare is all filled up we compare the fare distribution as below 4922, that we are done with our Feature Engineering We can now seperate thhe Training and Test Data from the complete data 5944, Our model created now we have to import test dataset 7443, Data Cleaning is now complete We can now use our data to build our models 12074, Feature Normalization 41272, Scientific Colormap 5316, Multiple Regression 41993, iloc To read certain rows 11221, correlation looks much better at the high end 39417, replace the NaN values in Cabin with Missing 13090, Finding categorical features and converting their pandas dtype to categorical ease visualization 22523, Evaluate models with different categorical treatment 43255, Pegando o log da coluna count 293, Embarked 18919, Embarked Feature 4091, In the search for writing homoscedasticity right at the first attempt 25861, Parameter Tuning of SGDClassifier 2402, Importing Packages 13887, Passengers Port of Embarkation 7524, Summary 27877, Sanity check 17905, Analyzing shapes of the dataframes 28479, Obtaining the absolute error from the logerror column 1554, Name 26709, Plotting sales ditribution across departments 9644, Again we can notice some type of deviation 32909, There is a very interesting article comparing the optimization function for this challenge 35123, Training Function 14244, Sex Categorical Feature 17721, Here we face the same issue as the age feature 30262, We read this array in a way that Columns are corresponding to our class predictions 10971, Top influencers 33507, Andorra 8539, Removing Duplicated Features 22847, Generating Lag Features and Mean Encodings 41029, We draw some interesting conclusions Pclass is definitely useful and also Embarked even though these two features are not independent 21537, Random Forest 39225, Correlation 2641, Preprocess data 11723, BernoulliNB 12022, Learning Curves 11531, Select Xgboost LightGBM Gradient Boosting Regression 6761, Checking Skewness for feature PoolArea 10860, Zooming up the map to list the top correlations with SalePrice 12758, For this part we evaluate our model using classification report and confusion matrix 25725, check a directory 24170, Evaluation 38274, define our model 2933, Combining Models with VotingClassifier 16040, We get our second error 24133, create Bag of word model it contains only unique words from corpus 35163, Experiment Data augmentation 28555, Treat missing values 20386, Create sparse matrix Bag of words 20269, Bang 10141, Simple Neural Network 3470, Here s a classification report 16850, Except the top four titles others are present in very few numbers and hence not fit to train the data on such small quantity 38169, You can also tell TPOT to export the corresponding Python code for the optimized pipeline to a text file with the export function and I personally think this is an amazing feature 21561, Embarked 261, Preprocessing and Data Split 30821, No overlapping 10201, Lets drop the columns with high perentage of null values 26797, Train the model using data augmentation 37755, Technique 6 Use of Generators 29794, Word2Vec 31534, LotFrontage 8443, Check if we had others TenC in the dataset 33269, Small peak at 64 and large peak at 451 they conrespond to periods 37986, Model3 fine tune model 1 add 1 fc layer 43125, SVM 411, Extra Trees 37335, train 17446, Actually i dont know if their is a reason for the tickets parch and SibSp 3345, lets visualize our newly created feature in waffle 18693, let s train our model for 4 epochs 16628, Select The Best Model 23479, Submitting Test Data 4672, We don t need Id feature so we drop it 15026, AgeGroup 41446, It s now possible to visualize our data points 11314, Survived 16960, Embarking Port 14355, Male and female distribution in Non Survived Passengers 29833, Target Prediction 24320, Convert some numerical features into categorical features 1300, Swarmplot 26510, Train validate and predict 5048, By the way If we want to visually combine count and distribution we can use a swarmplot 13518, To give us a better view of these transformations this is how our dataset columns would look like after the transformations inside a pipeline with Scaling and OneHotEncoding 6642, Survived and Not Survived by Age and Embarked 11751, Great We filled in all of our missing values 31862, We do the same with the validation data 32789, optionally you can also choose to drop the original categorical features 5912, Ensemble Blending 40722, Confusion Matrix 18938, Displays collected view of different categorical features with respect to single numerical variable 10416, Train the model 18354, ridge 20854, We can create a ModelData object directly from out data frame 19331, Utility Functions 5522, Estimate missing Embarkation Data 14681, Some important inferences 38901, Anatom Site Differences 40926, Simple Augmentation 12757, After trying several different models and even Artificial Neural Network with extensive hyperparameter analysis it turns out that the logistic regression performs the best which is extremely simple but effective 11943, Finding the most important features in the dataset span 15127, Correlation with Age Pclass Embarked 35548, to calculate the average weights let us look at the following code 11607, Overview 18399, All of the meta features have very similar distributions in training and test set which also proves that training and test set are taken from the same sample 42421, Correlation Analysis 14552, Parch Parents Children font 4219, Categorical Encoding 21088, Correlation plot 39773, the question ids range of the test dataset is sthe same as the question ids range for the train one 1396, Parch vs Survived 623, Deck 25465, Forward propagation 19368, Visualization of categorical attributes versus target 1110, Train the selected model 31406, We are ready to go now we can use standard fastai datablock API to create databunch 32349, Tokenizer 15423, let s have a look if port of embarkation affected the chances of survival 2183, Univariate statistics 27519, Show incorrectly classified images 26494, To output one of the images we reshape this long string of pixels into a 2 dimensional array which is basically a grayscale image 16581, Lets check which Fare class Survived along with their title 38588, Cleaning text in testing dataset 30405, Plotting prediction 28870, Inverse Normalise 8773, Label the Fare 21323, plot histograms numeric 18059, Word Cloud for Positive tweets 36270, Fare vs Pclass 32943, Check test and train now 15331, Mapping values 8958, Getting accuracy from the model 31858, Build XGB Models with GPU 40833, Adding dummy varibles to categorical data 726, In addition to the original values we have some new skewed values of the 2num variety 10040, Co relation plot 32049, Low Variance Filter 23389, the model is ready to be trained 33298, Feeding the Machine 28618, Exterior1st Exterior2nd 16620, Scaling 12183, that our data is numeric We can check the correlation between the features 32428, Training 2581, It is single layer neural network and used for classification 15202, that our data looks good lets get ready to build our models 41722, Training 28794, WORDCLOUD OF NEUTRAL TWEETS 36651, Plotting Functions and Resizing Images 15066, Family Size 35894, Submission 34718, Features found via LOFO importance 42418, Price Doc Distribution 17550, Age is the not very much determining factor for Survival prediction 3993, We got some values of the coefficients theta but we understand that they are obtained from data in which there is noise 34685, Target variable 16733, impute age 37550, Predicting on test set 15476, We bin Age into AgeBand so as to reduce noise 38995, Helper function to check out the properties of numpy arrays We be using this to validate dimensions of matrices throughout the rest of the code 15774, Categories 6123, Utilities 26807, Metrics 32146, How to find the grouped mean in numpy 38222, We replace the outlying coordinates with the average coordinates of the district they belong 14496, KNN With neigbhour and metric euclidean and accuracy score is and it is cross validated 28174, The dependency tag ROOT denotes the main verb or action in the sentence 31426, Implementation 13965, Sex Survival probability 3932, Exploring data 0, Missing Values Treatment 40274, Kitchen Quality vs Sale Price 27069, There is a little difference between keywords in train set and test set so I check the intersection between the keywrds in train and test 37537, Data Preparation 41852, Thresholding merged output 18978, Display values in table format Figure Factory format 41453, that we have a general idea of the data set contents we can dive deeper into each column 9822, Cabin 15221, So how great is 21040, Insincere Topic Wordcloud 11947, Handling remaining missing values by replacing it with median of the values 2339, Decision Trees 36086, Engine 20492, Client type 40711, Shape of training set 283, How many passengers travelled alone Were they more likely to survive compared to those that travelled with family 27611, Create the Dataframe for the Datagenerators 14324, Ticket 29043, Median subtraction 1908, Pivotal Features 31322, RMSPE Root Mean Square Percentage Error 6447, Modelling 32411, Preparing model and prediction 3180, cuml Models 39735, Age 18491, The StateHoliday is not very important to distinguish and can be merged in a binary variable called is holiday state 24277, Random Forests 21087, Descrictive Statistic Features 32157, How to create strides from a given 1D array 4838, Exterior1st and Exterior2nd and SaleType have just one missing values so we are just going to impute with the most common string 15051, LastName 30721, Confusion Matrix 32929, Saving model 22230, renme oran optimizasyonu Optimum Learning Rate 28934, Predict from data and we are done 6645, Survived and Not Survived by Embarked 25935, There are only a few variables that are object type and most of them are and no 11892, XGBOOST 3166, XGBoost 23554, have a look at how they have grown over these years 8391, Entities and EntitySets 42308, Predictions 5594, List of Machine Learning Algorithm used 20300, fare range increases the chances of survival increases 14515, Observations 31922, After trying multiple architectures the final one is 18348, To measure the completeness of the data present python provides a missingno function which help us visualize the complements of each variable present 12961, SibSp and Survived 29687, MaxPool This and some other similar layers actually makes our matrix smaller and smaller and less complex and saving only the important features with its location 28932, Creating Model and Fitting with multi gpu 8623, TotalBsmtSF vs SalePrice 1151, Creating and Training the Model 9656, Adding New Features 40749, Combine 33493, Linear Regression for all countries method 1 7384, For convenience I concatenate all matched passengers in one DataFrame merg all 9407, Neural Architecture Used 40419, Number of features 30313, Load dataset 30623, Pclass is a categorical male vs female variable 7532, lets take a look at Fare column May be it want to something to us 33872, Lasso 16655, Cabin 16153, Cabin 20080, Top Sales Item 24253, Cabin 1155, Gradient Boosting RegressioF 1822, A much anticipated decrease in mean squared error therefore better predicted model 41775, Model Building 3342, Using Median approach for Fare as it is also have few occuring 25779, using this features we can create new fature Family 42723, DAYS BIRTH is some high correlation with target 12675, Dropping old columns 2885, Year Built Features should have nice correlation with SalePrice 7463, Evaluating Accuracy of our model 3688, Normalise through Scaling 32480, that we have probabilities we want to remove the possibility of predicting a product that a user already owns 27283, Text Based Features 27920, Train and Validation Data Set 7643, combine train and test 39166, In fast ai the default is to use pretrained models If you don t want to use a model pretrained on ImageNet pass pretrained False to the architecture like this 27153, ExterQual Evaluates the quality of the material on the exterior 22062, Slang wording typos 36654, In Gaussian Filtering instead of using a normalized box filter a Gaussian kernel is used instead 10535, Missing values 23516, There are 4 elements in the class 40477, Polynomial Regression 40985, Passing a dictionary to agg and specify operations for each column 41843, Data Preprocessing 40034, Cross entropy loss 31524, Printing the confusion matrix for the same 1911, Missing Value Imputation 1904, Variable Identification 7529, xgboost optimization 15433, let s have a look how a Logistic Regression classfier is performing 32764, Before we move forward we need to check our dataset first if there anything wrong and the label are correct 27968, Reducing for train data set 6434, loc iloc 20604, Sex 31310, Missing Values 16687, Even though this is a very simple plot but we find out that women from Class 1 and class 2 have a higher survival rate and men from third class have a very low survival rate 26049, We then define device 7339, Random Forest 20166, Keeping 90 of information by choosing components falling within 0 9955, ML Models 3714, Alley 21839, XG Boost Model and Validation 12301, TicketGroup 6282, Final thing to do is create a flag to indicate the passengers that had a ticket with a prefix or not 6273, Fare 32212, Add lag values for item cnt month for month shop item subtype 14089, Feature Selection 19007, Look at the performance of the top 5 parameter choices 29036, Logistic Regression 1067, Averaging Regressors 21612, Shuffle rows of a df df sample 16236, Using MulticlassClassificationEvaluator we get the accuracy of our model 18656, Test Input Pipeline 27261, Use XGBRegressor as Second Level Regressor on First Level Features 5114, lets generate some plots related to dataset 12597, Function for model performance using AUC 3355, The only downside is we cannot track if the correlation is negative if you want to do it remove abs from the code 31622, Random Forest 29171, Check for missing values again 23820, Outlier detection and removal A bit cleaning 14598, Embarked 16233, we fit our pipeline and create a model This method is called Estimator An Estimator abstracts the concept of a learning algorithm or any algorithm that fits or trains on data Technically an Estimator implements a method fit which accepts a DataFrame and produces a Model which is a Transformer For example a learning algorithm such as LogisticRegression is an Estimator and calling fit trains a LogisticRegressionModel which is a Model and hence a Transformer 6724, Histogram 10278, One of the cool things about random forests is that you can get an assessment of which features contributed the most to the predictions 24656, Data window for forecast 40703, Predicting output for test data 7145, Gradient Boosting Classifier 38638, we get the outputs from the last fully connected layer 19873, look at the boxplot of age feature 27769, Adjust the bounding boxes 22213, Imports 17266, Observation 36267, Look in to relationships among dataset 36715, Final Submission 2119, Hyperparameter tuning 14994, Target Feature Survived 33862, Fititng Linear SVM with hyperparameter tuning 14514, Observations 4620, We have 3 types of data in our dataset Integer Float and Object 14976, Filling Age missing values of training test data set with Median 5162, Random Forest Regressor 24390, check if we have any NaN values left 43146, Sources of Data 35118, Creating NLP Augmentation pipeline similar to Albumentations in Deep Learning 10874, Modeling 14430, go to top of section engr 28176, Sentence Boundary Detection SBD 26875, target encoder LabelEncoder 13661, Family Size 14569, Feature Scaling font 16132, Build Model 2477, Scaling features 11308, Inspect Data Frames 39283, Discard irrelevant features 34391, Clearly there are differences in the occurrence of crimes through district 19571, training 29113, far this is what I tried 29606, We can do the same with the t SNE algorithm 8780, Cabin 10129, Support Vector Machine Classifier 27639, The may be the time to try and train a neural network on the top 25 fields most correlated with logerror 13038, Fare 20700, How to Accelerate Training With Batch Normalization 14389, Most of the Titles are Master Miss Mr and Mrs 3176, Trying to use all avaiable cpu cores with swifter 36043, create the following factors to get an idea about the affect from Population Population Density Median Age Urban population 5580, Model evaluation 23732, Survival rate is highest among middle aged men and women 34845, Ensemble CNN 18337, SUMMARY FROM GRAPH 8975, OnehogEncoder 9765, Feature Importances 29970, LSTM model baseline 41190, remove these columns from merged data 36551, Baseline submissions 18815, Base models 15801, center Models center 12097, Replace Missing 40065, There are negatively positively skewed columns 18178, Visualization utilities 7633, Boost Models 13597, check unique categories for each feature 40840, The outcomes in group 27940 changed only once in the timeline 30690, Tentativa Sem Autoencoder 4533, Numpy 25819, We also have some null values in the dataset 34002, season 22238, Gender 26722, For TX 33290, Fare Encoder 12698, To understand better what we are now working with the list size and values be printed below 29169, Exterior2nd Fill with most frequent class 2374, Area Under Curve AUC for binary classification ovo and ovr strategies 24693, Training 19721, Preprocessing Data 306, Light GBM 41832, Ensemble models 950, Takeaway from the Plots 1212, Observing Sale price histogram 6480, Pivotal Features 38005, we have the data about sell prices for all items in this store 14132, We decide to assign the N based on their fare 32808, Level 2 LightGBM 35115, Submit 29590, The final bit of the data processing is creating the iterators 42626, Difference between Lockdown Date and First Confirmed Case Date 1119, Embarked Missing Values 42536, Here I did all the steps for the test data as I did with train 14635, The first step can be to write a function that adds titles for the passengers from the Name column based on our findings in the EDA 20538, Linear Regression with ElasticNet regularization L1 and L2 penalty 27747, Missing data for train 22176, stack both the dense and sparse features into a single dataset and also get the target variable 29039, Submit 37740, A quick glance reveals many columns where there are few unique values relative to the overall 1902194 records in our data set 4361, There are lots of home with 0 value of BsmtFinSF2 feature 20338, Convert single channel grayscale images to 3 channel RGB 8665, Fillna and Feature Engineering 25019, Lung segmentation 13706, set up our cabin only dataset for use by a random forest classifier 27366, the shop name starts with a city name 38834, Prepare submission 34959, that I have my clusters I can attached them back to my original dataframe 26806, Dataset 8727, Sale Price and Overall Quality 22274, The most important part of each value is what cabin letter they are in 32751, We can join the calculated dataframe to the main training dataframe using a merge 3941, Combining train and test data in order to make imputing missing values easier locating missing values 17430, For my idea this means 34286, Feature Reduction 23444, Line plot for all continuous values in file 13049, Creating a Model 39150, perform normalization to make the CNN converge faster 32653, Replacement strategy p 17444, just for today 869, For passengers in Age bin 1 All male in Pclass 1 and 2 survived 28125, Data Cleaning 28656, Since this feature is based around local features it is understandable that having more desirable things like a parks 19281, Inspect the structure of our model 31540, Categorical Features 18009, Prediction and submission 26977, Submission 259, Plotting Clusters 41061, Train XGBoost 10447, Convert Categorical Variables into ordinal numerics 8746, Train Validation Split 3449, Decision tree based imputation of missing Deck values 26519, Visualising the distribution of each product by age by boxplot 23078, Outliers 10624, do some more EDA 40003, Missing values 35878, Final prediction 26846, Second batch 38003, All info about a single store 37089, The shapes look familiar 4759, heatmap is a good way to understand correlation 35076, Performing the second step of the training process 27568, ps ind 10 13 bin 17725, In the test data there is 1 empty value in the Fare feature 2981, Hypothesis Testing 12702, The mean and percentile breakdown indicates multiple features converging around the 30 mark which perhaps isn t surprising 3321, The main reason to have seaborn apart from matplot lib is It is used to create more attractive and informative statistical graphics 32583, Automated Hyperparameter Optimization in Practice 38692, n images 16895, New Feature Title 5664, Create a Dictionary to map the Title s 3653, Dropping redundant features 28533, BsmtUnfSF 27504, First Import Required Library 16708, collect our splits 23608, Training Function 17001, Explore Dataset 6126, Time to bath not bass 14148, Voting Classifier 24821, BERT Text Encoding Sample Example 5950, Loading the data files 42563, Another step that many others have already done 40981, Cutting values into 3 equal buckets 1696, Detecting Missing values 27193, we compile our Neural Network 21614, Concatenate 2 column strings 6009, Gunakan target yang sudah di Scaling dan Transform 35434, Split data into features pixels and labels numbers from 0 to 9 37338, load weight and Prediction 19908, Days 10417, Permutation Importance Importance of various features 10958, Data types 6314, Support Vector Machine 38093, PREDICTION 18325, test one more set of parameters 2364, Sklearn Voter Pipeline 23735, Title Feature 28179, Using this technique we can identify a variety of entities within the text 31856, Final preparations 17041, One hot Encoder 21546, our final model becomes 20258, Variables 27645, XGBoost otherwise known as eXtreme Gradient Boosting is a great resource to train gradient boosted decision trees fast and accurately 43067, check now the distribution of max values per rows for train and test set 27155, Category 8 Heating and Air Conditioning 31478, Generate test predictions 12703, now we have a completed view of Age it makes sense to visualise it 40788, Even after averaging the structure inside the week is lost when averaging over all places 39038, Anyway grouping by lon lat may also give some problems 30656, Seems like we didn t remove all repeated topics but reduce the number of unique values from 222 to 186 4739, Missing value of each rows 3818, We know the mean age on the Titanic was about 29 26017, Home functionality Assume typical unless deductions are warranted 16462, As much you pay that much you get security 19376, Machine Learning Algorithms 28203, The Corpora 12185, Training models 21441, Mixture w OverallQual and OverallCond 6752, Checking Skewness for feature SalePrice 23207, Advanced Ensemble Methods 24002, Blending Models Ensambling 28582, 2ndFlrSF 23568, try out the model 17047, After submiting each of this solution to Kaggle discovered that cross validation scores are higher than on public leaderboard meaning we are overfitting to training set 22701, Random Vertical and Horizontal Shift 14897, D surface plot and contour plot to visualize the relation among SibSp Parch and Age 4065, The average silhouette score is maximum when K 3 12086, Split data in train and validation 80 20 23738, Fare per Person Feature 13912, Before we proceed further with Decision Tree we need to do some cleanup 21467, Parametrization 15086, Bagging Classifier 4174, The variable Age contains missing data that I fill by extracting a random sample of the variable 9012, Therefore we take the average pool quality of pools that are around the same Area of the Pool and set the pool quality manually to whatever the average pool quality is of pools that are that size 34174, The UpOrDown variable 29596, To prepare to use the range finder we define an initial very low starting learning rate and then create an instance of the optimizer we want to use with that learning rate 27510, fitting train and test data 21415, Feature primitives Basically which functions are we going to use to create features Since we did not specify it we be using standard ones check doc There is a option to define own ones or to just select some of the standards 3222, A pie chart is a circular statistical graphic which is divided into slices to illustrate numerical proportion 13930, Exemple Wilkes Mrs James Ellen Needs 10461, It would also be interesting to understand strong correlations between attribute pairs 36067, Predict Test Data 21025, Visualise the data 24882, No one else with that ticket 8973, fill values according with correlation 3858, split training data for crossvalidation 31012, I use ImageDataGenerator from keras to augment the images 1801, Assumptions of Regression 25265, Data Pre Processing 1819, we have calculated the beta coefficients 23277, Public LB Verification 26001, Predicting the category of San Francisco crimes 15553, Logistic regression 28519, TotRmsAbvGrd 6155, Stacking on training set 36037, Correlations between features and target 9269, To check if any feature type is misclassified 1279, Since 72 19612, Handle missing data 28954, I ll break the categories roughly up into the quartiles 28491, Clearly these three columns can be converted to type int32 26873, let s try to visualize some feature maps for our convolutionnal layers 12014, It clearly implies that desicion tree have a tendency to overfit the data quickly 38972, train list total length is 7613 22838, Generating prodcuct of Shop Item pairs for each month in the training data 29681, Shuffling data and splitting into training and validation set 34735, T SNE applied to Latent Semantic LSA space 14796, Decision Tree Classifier 33261, Build CNN Model 25401, It can be a problem because Neural Network uses a lot of sum 15513, This is highly incomplete 28632, GarageFinish 8952, Fixing Pools 12423, we are ready to go for submitting this vanila model 24698, create two evaluators to compute metrics on train test images and log them to Tensorboard 11691, The accuracy of Random Forest classifier as reported by Kaggle is 76 19038, Specify the source 37810, Read the files 15689, Create two functions to plot the of total counts and of survived dead passengers 31286, Prophet 33029, Prediction and submission 11756, Hyperparameter Tuning 4962, Loading Libraries 29224, For Training Data Set 24232, Start training the model 29183, Define IVs and DV X y 21645, Create a datetime columns from multiple columns 19340, Top Questions 37628, The ToFloat transform divides the pixel values by max value to get a float output array where all values lie in the range documentation 16489, Dealing with MISSING VALUES 40827, Biivariate analysis on continuous data 26241, Preparing PL Model 1 12914, Check for missing values 5681, Fill the missing Age values by calling the routine fill missing age 29589, The filters are still 2 dimensional but they are expanded to a depth of three dimensions inside the plot filter function 15827, for scaled data 210, Libraries and Data 15064, Data exploration 39730, Sex 19289, Crop function 43242, Training 12356, BsmtUnfSF Unfinished square feet of basement area 7925, Select train and test dataset 10943, Structure of test data 1203, Belows function was used to obtain the optimal boosting rounds 21360, Only minimally But that s fine if they were equivalent we wouldn t need this additional target at all 3001, Linear Regression Model 18679, Evaluating for associated model on dev data 12949, Individual Classifiers 31539, replacing with median value 26863, This last filter acts the same way than the two previous one it s a gradient 35179, Final model and submission 20743, GarageFinish column 8046, Relevance of features target 13405, Confusion matrix 20051, shops id 0 1 and 11 are not in the test data I think we should merge the shop id to solve that problem 21646, Show memory usage of a df and every column 24667, Make Predictions 10993, Import necessary packages br 3635, Embarked 34837, Convert the categorical columns to numerica column using the one hot encoding 39215, Create the neural network 34010, humidity 40647, TFIDF W2V 41172, test samples 11147, Cross Correlation chart to view collinearity within the features 43257, Juntando os DataFrames 7448, Examining the Distribution of the Target Column 7819, KFold 31263, Sales data 38105, Compiling and Fitting the Model 41938, define helper functions to reset gradients and train the discriminator 31375, Add noise 16855, Visualization 21492, LOAD DATASET FROM DISK 15974, Fare Feature 3297, Splicing data and visualizing the number of missing values 5517, Random Forest Classifier 14871, we know that if the Alone column is anything but 0 then the passenger had family aboard and wasn t alone 40868, Optimize Lasso 28666, LandSlope 39874, total and garage 8838, Add features 43059, Distribution of mean and std 36493, Start each fold with different groups 39174, index 27476 30598, Correlations 13119, ROC and PR Curves 11769, Loading Data 28709, Start the TensorFlow portions and 1 hot encode the labels 26465, Keras library provides module for generating mini batches of augmented data that we can directly feed into our CNN model which provides convenience as we just have to feed in the dataframe with relevant file path and labels 5530, Lone Travellers Feature 4985, Surviving rates per feature 37797, Train Test Split 25166, Asking Some Basic Question To Our Extracted Feature 5109, Feature Transformation 3411, Create new variables related to family size 42039, where 35834, finally check if there is anymore missing values 31079, LotFrontage Linear feet of street connected to property 35431, Creating the output csv file 6335, news Most of the features are clean from missing values 41111, DateTime Parsing 18126, Cross validation can be used to find optimum hyperparameters 27911, Examine Numerical Features that are failed 8532, Target Variable 20664, WE LEFT HERE THE PARAMETER OF MSZONING 14411, I ll check the accuracies of all the models with KFold Cross Validation and choosing best hyperparameters with GridSerachCV 5533, Deck feature 22101, Convert our Test Data to Tensor Variable 43384, check if we have as much weight vectors as classes 42939, Removing data we no longer need 3855, Feature Deck 15609, Estimate missing Embarkation Data 7356, Drop irrelevant features 25200, we have completed all the pre processes its time to train our model 14308, Selecting Feature from training set to feed to the neural networks 13502, Baseline model 17953, K nearest neighbors Model 41968, Map the Items 10611, Age 17909, so there are 2 NaNs to take care of in data train 12436, Building pipeline 28856, Seed All 36721, Change the first convolutional layer to accept single channel input 35883, Distribution of the response 4294, Add Neighborhood Dummies 42286, Display Data Augmentation 16563, Summary based on visuals 32655, Skewness may be interpreted as the extent to which a variable distribution differs from a normal distribution 10649, SibSp and ParCh 13956, Test data 41185, look into missing value counts in categorical columns in training data 992, let s follow a slightly different route We concatenate our train test datasets first 21225, As we load in our data we need both our X and our Y 689, Encoding the categorical features below is required since the machine learning algorithms work with numbers and not with strings 27740, Missing data 9947, One Hot Encoding of features 4935, Pre processing and Feature Engineering 5067, To get a baseline for all other experiments we setup a dummy regressor that simply predicts a constant that we define e 27929, Data pipeline 24884, This fancy function pulls Family name from names of people and makes a new Family column 193, Lasso Regression 33500, Andorra 15547, filling 24964, Accuracy score 3965, Lasso Regression 8236, Exploiting a datetime feature 2113, This is indeed a clearer signal and we should consider using this feature and dropping the other bath features 9515, Age 2462, Fisher Score chi square implementation 40031, Dataset 11288, Numerical Features Replace missing values with 0 518, loading the data 10804, we have now all data in Embarked column 13572, Keep thinking about Familys 3551, Allright the first thing that we conclude is we should take advices from our mother seriously 38672, Perceptron 28695, Treating skewed features 2966, a slight variation in the K Fold cross validation technique is made such that each fold contains approximately the same percentage of samples of each target class as the complete set or in case of prediction problems the mean response value is approximately equal in all the folds 26589, TASK EXPLORE SALES TRAINING DATA 24393, Predicting values 40719, Compiling Model 30983, First we can plot the validation scores versus the iteration 29885, Analysis of punctuation marks repetition in text 1362, The next model Random Forests is one of the most popular 40390, Defining the dataset 33489, Italy 2672, Mutual Information MI 39128, Print shapes of each dataframe so we don t make mistakes when fitting the Neural Network 38654, Fare 24135, start with Gaussian Naive Bayes Model 41834, Submit task 23215, bayesian optimization part 21840, Write output CSV file of predictions from test data for contest submission 31942, Here are some examples of the predictions made 42408, Random Forest Classification 19893, Split train and test data 42791, Observations 5083, We again have a look at the basic statistics of sales prices in our training data in order to compare these to stats of predicted values 18076, Area of bounding boxes 34832, Drop Column with most null values 29702, Getting Predictions 8089, Identify the best performing model 18256, Fine tune the complete model 3197, Not much left I don t want to think about it anymore 21624, Ordered categories from pandas aptypes import CategoricalDtypee 13391, Correlation of features with target 7353, Correlation more than 0 26867, Learned filters visualisation 39405, Getting the data 33439, XGBClassifier 26808, Loss 13674, We have nulls in Age Cabin and Fare in the validation dataset 22861, ISOMAP 39745, Imputing Age 8487, Passive Aggressive Regressor 39387, Replace null values with median values 20943, Import necessary libraries 40138, Create or retreive dataset 24544, Since 6 out of 160 channels account for about 87 3612, Data Cleaning 11307, Importing the input files 16008, Model 33800, All three EXT SOURCE featureshave negative correlations with the target indicating that as the value of the EXT SOURCE increases the client is more likely to repay the loan 8571, Grouping Rows by Time 133, Grid Search on Logistic Regression 25908, Generating tweets about real disaster 7151, HIERARCHICAL INDEXING 15521, Modeling and Prediction 7378, Merging the unmatched passengers using surname codes and name codes 16481, Looks like 1098 people did not survive while around 684 people in both the dataset survived 29377, Data Augmentation 303, need to scale in case we want to use linear models 23448, Working day 8543, Missing Over 50 26927, now we are up to the key method of preprocessing comparation 31682, The classifier acheives good accuracy on images with no noise 27003, Treated text 10238, To get best parameters for Random Forest let s do Grid Search for Random Forest model 30756, Final set of parameter 1826, Lasso 35455, checking the correlation between features and Target 13052, AdaBoost classifier 451, Creating train and test data 32976, Lets create one feature variable Status in the society This features can be derive from the name features like Dr Rev Col Major etc 37278, Using the gensim function to load in the embeddings ended up being much more time efficient than most of the things available in the public kernels 33046, Neural Network with two layers using neuralnet 26755, Prepare data 8876, Missing Data Handling 41850, First Patient Merged Output 19631, HOW THE ALGORITHM WORKS AND WHY MY FEATURES DON T POSITIVELY IMPACT IT 7112, Dealing with string values 35068, Complexity graph of Solution 4 2 3030, Created 3 models RidgeCV LassoCV ElasticNetCV and are linear models with built in cross validation 17709, do some visualizations 20875, In contrast to other CNNs Inception Neural Networks allow more efficient computation through a dimensionality reduction with stacked 1 1 convolutions 7215, Missing Values Imputation 32981, Preview 23835, There are different number of categories in train and test datset 9132, Therefore we take the average pool quality of pools that are around the same Area of the Pool and set the pool quality manually to whatever the average pool quality is of pools that are that size 28768, Saving 43370, Changing labels to one hot vectors 27494, Networks 25409, Where N is the number of the exemples t is the right labels and y is the predict labels 9740, Features Visualization 29959, Prediction 12370, Defining what to replace with 6654, Corelation of all the attributes by Heatmap 26656, Applying Deep learning model to predict the image 10984, To the next Step 41395, CODE GENDER 28180, Similarity 22683, Our model 15973, As this feature is alphanumerical and it depends on the embarking port prices and more features we drop this feature 3870, the ouput of both impute mode cols and impute NA cols and concatenated by using make union this is helper function to the FeatureUnion in sklearn which can be used to concatenate outputs from multiple pipelines transformers check out the shape of categorical data pipeline 14086, We now drop the Cabin Column as it contains too many null values 4128, We take the weighted sum of the predictions of the three models to form a ensmeble model and submit the ensemble predictions in a submission 42930, Saving the target and ID code data 7152, STACKING and UNSTACKING DATAFRAME 6794, Naive Bayes 100, While passenger who traveled in small groups with sibilings spouses had better changes of survivint than other passengers 38911, Validation Without Some Features 23958, First Few Rows Of Data 36117, Lets have a look to our new cleaned data 3478, look at the score and the parameters for the best model 11122, Model Testing Only catagorical Featues 20196, Chi2 Test 5961, Fare and Survived 39679, We save the final image produced by our model as a 7906, th Model RidgeClassifier 19313, Evaluation prediction and analysis 24023, How to form evaluation sample 15083, Gradient Boosting 41665, The training dataset is split into training and validation samples that are to be used when assesing the Random Forrest models built 27649, Housing Prices 2830, combining the 3 models to predict on the test set 1389, look this keys values further 32631, Making the submission 24762, Again i ll be using RobustScaler to scale all features before initiating the ElasticNet model 10544, Ridge 25829, Loading Libraries 36374, check the number of non duplicates vs the number of duplicates 8586, Deleting not useful features 36369, Create submission file 21574, Moving columns to a specific location 26643, check how many topics and docs are described in this file 3204, Feature Selection from EDA 5024, Errors 20663, HERE WE BEGAN WITH SOME INCREASE AS IN THE CASE TO MODIFY THE DATA GIVEN AS NAN 13888, Family members 20190, Continious Featuers Box Plot 24938, StandardScaling and MinMax Scaling have similar applications and are often more or less interchangeable 16955, The passengers age distribution indicates that the majority of passengers are young in their 20s and 30s 39683, Text Cleaning 24146, Clean Organize 39304, XGBRegressor validation 34074, Age is not correlated with sex but it is correlated with parch sibsp and pclass 35667, Removing features that have mostly just 1 value 33998, count 32907, Here we defined the input shape 40636, Text feature 3266, Visualizing some highly correlated features to get better understanding 40029, There is indeed a big group of test images that is not presented in train 31307, The Date column is of the object type we need to convert it to DateTime 22677, Setting the Bert Classification Model 20522, SalePrice relationship with categorial features 43261, Separandos os conjuntos de Treino e Valida o 9481, Classification Report 43241, Defining the model 21154, not bad already great improvement 40742, Larger CNN 30683, Make a special function for LightGBM 11971, Numerical Features 23917, LDA 36797, Negation 41347, Visualization 7522, Model 3 A univariate quadratic function y ax 2 bx c 35353, Preprocessing data 10605, Step 5 Fit Your Model Predict Know accuracy on Validation Set 4390, From histogram of test data there is a home exist with more MiscVal value 40736, Flatten 18280, Hyperparameter tuning using RandomSearchCV 27859, Feature importance 1548, The goal of this section is to gain an understanding of our data in order to inform what we do in the feature engineering section 1106, Gradient Boosting Classifier 32019, There is one Ms so we assign Ms to Miss category and NaN to all remaining rows 6149, Divide full dataset into train and test subsets again 22007, Use the next code cell to label encode the data in X train and X valid 25467, Back propagation 12204, In other words with the use of the sklearn Pipeline we want to sequentially apply the transformations in the given list 7487, 4c Age Groups 43025, Do some final cleanup and split into test train 41585, Similarly unffreezing the last 2 blocks of the VGG16model 42214, up I add in a Max Pooling layer 23114, Ticket is also an alphanumeric type variable 19775, Set batch size not too high 16219, make a new column which store the values of number of person in a family and another column which tell whether the person is alone or not Then we visualize it so that we can check if survival rate have anything to do with family size of the passengers 43391, The gradient travel guide natural fooling targets 26298, NOTE These plots are more useful when trained for more number of epochs 20472, Days ID publish distribution 3878, ColumnsEqualityChecker returns a FunctionTransformer that wraps the function equalityChecker Any arguments to equalityChecker can be passed in kw args of FunctionTransformer 33792, Well that is extremely interesting It turns out that the anomalies have a lower rate of default 7857, Feature Pruning 38970, first lets find the maximum length or no of words in a text column of train and test 1925, Pool 21253, My Data Processing and Binning Functions 7140, Embarked Title 28706, Ensemble 12295, Age 25639, Ensemble modeling 36404, And the feature importance 4783, Having a look at the correlation of different numerical feature with SalePrice 6288, Model comparison 13595, Check Accuracy 11026, Creating an artificial feature by multiplying age and class 41779, Models created with Keras and most other deep learning frameworks operate on floating point numbers 13200, When we use get 251, Model and Accuracy 16663, Exploring Correlations 5229, Filter 3270, Updating Garage features 11416, Use Case 8 Sales Projections SunBurst Chart 35192, we have got the list of top 30 important features 42617, Before we train the network we need a function that creates randomized batches of training data so that we can implement mini batch optimization 20657, FROM THE TWO FACTORS SHOWN TO BE CORRELATED REMOVE ANY ONE OF THE FACTORS 16681, Describing Data 11136, SalePrice is the variable we need to predict let s do some analysis on this variable first 32403, K fold Split 8013, Stacking Our Model 24137, Gradient Boosting Model 3970, Stacked 22100, Import test data 21423, TotalBsmtSF 6778, The Chart confirms 1st class more likely survivied than other classes 16203, The next model Random Forests is one of the most popular 41118, Geographic Location by Folium and Cluster by KMeans 20390, K Nearest Neighbors Model 40782, The standard way to avoid overfitting is called L2 regularization It consists of appropriately modifying your cost function from 11624, Normalization Standarization 41746, MLP ReLU SGD 15379, Much better 18301, let s checck which items have item price 0 25488, Fill missing values 31688, Plotting predicted class along with the images 11900, Data split 2027, Submission 26539, Like I said the model is no where near convergence The plot is for 30 epochs trained on my system MY advice train it for atleast 50 more epochs with early stopping 40133, Fitting models to the combined dataset with custom pipelines 5990, Missing Value 1376, lets start explore the data 33606, Data Preprocessing 20263, Logistic Regression 5572, Data Scaling 7113, Dealing with correlations 19406, Checking the Performance 8523, Submission 17811, prepare a simple model using Random Forest 23199, there we have it We re down to 2 features only from 47 features we want to calculate how much variance we re able to extract off these 2 components 945, Evaluate Model Performance 4938, Handling categorcial missing data 29393, To better analyse our data let us plot its performance as a function of no of epochs 11275, Looking at the values it looks like we can separate the feature into four bins to capture some patterns from the data 39976, Data Loading 27521, Submission 41080, LSTM with glove 6B 200d word embedding 7736, Ensemble methods are commonly used to boost predictive accuracy by combining the predictions of multiple machine learning models 3149, Afetr tunning i got best fit parameters and model accuracy of percentage which is quite good With this submission my Public Score is Let do the CV and tunning with some other models and check the accuracy 15038, Like the income of resident the distribution is usually left bias compress it with a Log Transform could be more normally distributed 28141, Entities Extraction 13190, let s take a look again into boxplot relation between Fare and our target Survived 25487, Define which of these training features are categorical 28902, Same process for Promo dates 29218, Implementing The regressors comparing accuracies 35126, Handling Outliers 16611, Feature Embarked 36899, Adam 23414, define two useful callbacks one for the model checkpointing and one for managing the learning rate policy 24786, Oof predictions 13602, Similary we fit it onto training dataset 39297, Feature analysis 919, One Hot Encoding 23211, now we have OOF from base or 0 level models models and we can build level 1 model We have 5 base models level 0 models so we expect to get 5 columns in sTrain and sTest sTrain be our input feature to train our meta learner and then prediction be made on sTest after we train our meta learner And this prediction on sTest is actually the prediction for our test set xTest Before we train our meta learner we can investigate sTrain and sTest 6711, Find Categorical Feature Distribution 4016, We use our own ridge regression algorithm 13153, Bin the Age and Fare variables now 39125, With Keras 10976, Linear regression elastic net 26969, DataLoader 42533, Extracting the day of the month 18277, TF IDF VECTORIZATION ON QUORA QUESTION PAIR SIMILARITY 31265, Wavelet denoising 19074, Survived 14458, go to top of section model 11873, Y target value to Log as stated at Kaggle Evaluation page 11002, SibSp 28515, GrLivArea 6700, Changing Labels 1263, Get cross validation scores for each model 38269, Remove leading trailing and extra spaces 8687, Most Related Features to the Target 10103, we have to create a Machine Learning Model 24444, Visualization of data 5706, Utilities For this categorical feature all records are AllPub except for one NoSeWa and 2 NA Since the house with NoSewa is in the training set this feature won t help in predictive modelling We can then safely remove it 31518, we need to implement train test split and feature scaling 202, TheilSen Regressor 13378, Imputation of missing values in Age 4186, the missing SalePrice are the one we try to predict as it match the number of rows in the test set 15931, Sex 35242, We cannot remove stopwords from this variable 7943, First try with a basic MLP 41849, ROI detection 16991, Combining classifer 26200, Reading and Loading the Dataset 39033, Test 25187, Data Preparation 974, Compare the models 417, Dealing with outliers 10385, Understanding our data 11781, Age 10029, RMSE on the entire Train data when averaging 27415, Visualizing and Analysis 42443, Features that belong to similar groupings are tagged as such in the feature names e g ind reg car calc 8890, Model Training 5984, Voting Classifier 6085, Prediction 6615, Label Encoding 21580, Convert one type of values to others 29695, 4 kernels from every convolutional layer 4046, We can visualize the titles over a histogram 10816, The highest values came from Sex PrachCat and SibSpCat 11362, MSZoning The general zoning classification RL is by far the most common value we can fill in missing values with RL 24017, Some examples with predicted label 30822, Partial 50 overlapping 3996, Compare our implementation of the algorithm with the LinearRegression method implemented in sklearn 33768, Split Data into Train and Validation 29813, Vector Averaging With Word2Vec 22332, Convert Negative Word to its Antonyms 3257, em A Comparison between Violin Plot and Box Plots em 20198, Correlation 24006, Split labels and features of training dataset and convert to numpy array 37698, Gradient descent 21659, Split a df into 2 random subsets 38147, Inputs to MLBox 37730, Upsampling the data 24836, use SVM 42034, Groupby Count 38268, Removing punctuation marks 42997, Linear SVM with hyperparameter tuning 21329, Taxs value 291, Additionally there are many missing values for Cabin 832, new dataframes 30686, Make DataFrame 36544, Linear correlations 31842, etc etc 15437, the AdaBoost Classifier 21081, Missing value in test train data set are in same propotion and same column 32476, De duplicate the Data 29829, CNN 14557, Lets explore some real data on the combined data titanic 5026, Ridge Regression 36620, Data Manipulation 684, Preparing the data 4861, Top 20 variables correlated with SalePrice with score 16029, the scale goes to high because too many Z value 25503, Use the next code cell to one hot encode the data in X train and X valid 35114, Find best threshold 26261, Plotting the confusion matrix 7389, the corrected dataset for all merged passengers merg all2 is obtained 37946, We have already noticed that we do not have many features available 30832, Is there any null value present in the train data 27935, With the model trained I ll plot the accuracy achieved per epoch per dataset on a chart so that training progress is clearer 29820, Trained skipgram 10459, We d like to know how each input attribute is able to predict the target e 37823, Remove Stop words 10621, Dealing with Cabin data 4368, 2ndFlrSF feature is linearly correlation with target SalesPrice 731, Immediately two points jump out both of which are GrLivArea the most important feature 34655, Merging everything together 22976, Most store are in a very close competition 12524, Filling the missing values 19310, Evaluation prediction and analysis 34241, GPU use 28214, checking for difference between survived and not survived means 29842, looking at the corrolation 2878, u Class and survivorship plot u 33586, Running inference 7530, Age distribution is positive skewed Need more information to fill missing data plot Age with PClass 27528, Display heatmap of quantitative variables with a numerical variable binned 38137, We be predicting this with svm 19731, For each Category 26689, FLAG DOCUMENT TOTAL the total number of provided document 14407, Deleting Fare feature because now we have FareBand 31576, Target 38553, Tagging Parts Of Speech And More Feature Engineering 29960, Demonstration how it works 1541, Sex Feature 5577, Linear Regression 31610, STOCHASTIQUE GRADIENT DESCENT 35051, To begin with I try a fully connected NN consisting of a layer of 512 neurons followed by a dropout of 20 followed by another layer of 512 neurons and another dropout of 20 and finally a layer of 10 neurons with softmax activation 35567, 5 failure rate for 100K rows 24871, Neural Network 37042, Model training 26980, Save labels in a separate 1D array 18288, Tokenizing 10076, To rescale our data we use the fonction MinMaxScaler of Scikit learn 20781, Understanding the Data 32110, How to limit the number of items printed in output of numpy array 3697, Omit irrelevant columns 8116, Decision Trees 38931, concatinating both train and test dataset to convert categorical data into numerical data 35492, Creating Submission 36391, The NaN values have now been replaced by the median value for each category 11299, test out some common models for regression 2309, Pandas how to apply a function row wise example is adding strings 14462, back to Evaluate the Model model eval 34852, Inspection of Class Balance 32346, No Of Storey Over The Years 37104, Feature Importance 14997, Outliers Detection 820, List of numerical features and their correlation coefficient to target 29607, We can also imagine an image belonging to a specified class 20797, Filling Missing Values in LotFrontage 26420, The best strategy to fill the missing values might be to use the titles to guess the age since we are goint to divide Age in two categories namely 15 and 15 years 4300, Inference 5737, LightGBM 15947, Embarked unvan 5150, Categorical variables 10428, Comparison and merging of all feature importance diagrams 5926, how does standard deviation sigma affect data analysis 5532, Cabin feature 17564, Correlation between Variables 9190, Fare 22702, Load the Data into Tensors 30649, Machine Learning Model Selection Submission h2 27, RandomizedSearchCV ElasticNet 1871, Loading and Viewing Data Set 35193, Feature Transformation 14239, Embarked Features 35057, In this solution I implement a Convolutional Network to replace my Fully Connected Neural Network from solution1 16446, Sex 2776, Light GB 33091, Linear regressor 38568, Feature Agglomeration 40866, Being root mean squared error smaller is better Looks like SVM is the best regression model followed by Ridge GB and XGB Unfortunately LR can t find any linear pattern hence it performs worst and hence discarded 15443, Columns SibSp and Parch are very similar to each other in meaning and in correlation 38770, We are planning to train our model iteratively 5503, Imputing the Embarked 36455, Submission 22596, Test set 10052, We can drop Ticket feature since it is unlikely to have useful information 13218, Data Cleaning 3436, start by cleaning up the categories a little 37376, code to create submission file 18013, NaN by feature 17778, Multiple features visualization 31070, UNIQUE TECHNIQUE TO SEE MISSING VALUES 32669, The accumulation of features with zero as their predominant value may lead to potential overfitting 31329, Submission 42448, Metadata 40672, Histogram plot of Number of words 22349, Logistic Regression 33680, Difference Days font 1044, Outliers detection 36006, Review Data 1089, have a look at some key information about the variables 9248, Filling Nulls for each column appropriately 32555, Simple EDA for Duplicates 4699, Missing LotFrontage prediction 15915, However some common surnames may be shared by people from different families 37360, SibSp vs Survived 37557, Load path 19442, As the pixel intensities are currently between the range of 0 and 255 we proceed to normalize the features using broadcasting 11777, Deleting Unnecessary Variables 33736, One note on the labels 38477, Image Filters 23602, This is where the initial usage of gensim begins 26443, In general the height of the score might not be the best measure to evaluate the performance of a classifier especially if the dataset is skewed e 6878, started by importing our libraries 1895, LinearSVC Model 26310, we are going to pick some features for the model 23312, After merging we have lots of missing values of item cnt month 35125, The columns CompetitionOpenSinceMonth CompetitionOpenSinceYear Promo2SinceWeek Promo2SinceYear PromoInterval have too many values as NaN 22388, I wanted to make some attempt on the y data 26993, If you apply lowerization you lost a bit of informations on other embeddings 26280, Extracting numerical columns and fill all NaNs with 0 in our DataSet 41839, Meta Data 9628, Random Forest Best One Without Feature Engineering 6387, find the average value for Survived column for male and for female passengers 8463, Wrapper Methods 16943, This random forest model is way better than the single tree model when comparing the rmse score on train data 19044, We can also view the pixel distribution of the image 42151, Decoder 26710, Plotting sales distribution of stores across departments 13105, Naive Bayes 8542, MISSING DATA 36487, Load Embeddings 1630, Splitting to features and labels and deleting variables I don t need 27989, Chart analysis 27454, Replace Contractions 27474, TFIDF Features 25042, merge these product details with the order prior details 6250, Cabin 4725, for total number of rows and columns we use pandas 26349, These are the columns with the best correlation rate visualize them 30665, Checking the function on three samples 17654, Stacking models 2556, Lets prepare the data for H2O automl and select only important columns 5551, Make Predictions 10779, Area Columns 36419, Finding Percentage of Missing Values 15863, Feature Importance 27605, To clean up the noise in the image some libraries from skimage are needed 20727, Exterior1st column 36278, many different values let s place missing values with U as Unknown 2342, Random Forest 16730, by fare 36388, Train and measure the MAE with the custom encoder 41844, Data Cleaning 28522, GarageYrBlt 6275, Title 4017, Data Engineering NaN replacements 2971, Bivariate analysis 20612, Embarked 38755, The train accuracy is 82 42215, I be adding in further Convolutional layers shortly however before I define a Dropout layer of 0 37834, Final Submission File 10389, Visualizing the relationship between SalePrice and SaleType 15902, Fare 8554, Series are 1 D arrays it is like one row dataframe 315, Age 25873, Number of tweets according to location per class 0 or1 30564, Initial start only with XGBoost 10534, scaling 7405, Correlation analysis 39157, That means that for every image in the test set it predicts how likely each class is 17022, Name 36143, First we again have to define the dictionary 14684, Most of the passengers were from Southampton Southampton was the starting port of its journey 11732, Submit 33317, Eigenvectors and Eigenvalues 22113, Learning 2044, Defining Features in Training Test Set 34055, Fit Make the predictions 24983, Filling NaNs in numeric columns using mean for each column 14908, There s only 2 missing values in Embarked 7106, Support Vector Machine 20069, Converting class type to reduce memory load 2226, High Correlated Variables with SalePrice 36607, Training our model 14793, Reusable functions 40156, Continuing further with missing data 21493, Load Embeddings 32947, Check correlations 35464, Visualiza the skin cancer at Oral genital 35111, Creating keras callback for QWK 26735, Plotting sales over the week for the 3 categories 25770, this all are unique titles we got from names 4950, Remember when we combined the training data and the test data to make one big all data dataframe 13583, Setting X and Y 7770, Decision Tree 36915, Exploratory Data Analysis 3765, Almost Similar to ridge let s try on normalized data 42042, Insert a value for NaN 15799, Correlation 17375, Great 2945, check features with high correlation only 25173, Last Word Checks if last word is same in both Q1 and Q2 8136, Lasso ElasticNet Ridge and Xgboost gave the best score lets test voting on them 14006, Feature Selection 19944, Filling missing Values 21331, bathroom features 19764, For explanation purpose it makes sense to create a dataset containing the number of each word in each document 12293, Embarked 36259, analysize Pclass 29854, look at the score of each hyperparameter combination tested during the grid search 16185, Create new feature combining existing features 20310, We have found a possible clustering for our customers 25735, check our model performance and visualize some data 37627, In PyTorch the best way to feed data to the model is with a dataloader In particular torch utils data DataLoader is an iterator which provides features such as batching shuffling and loading data in parallel In order to use PyTorch s dataloader we need to create a dataset first The most flexible way to do this is by creating a custom dataset class that inherits from torch utils data Dataset which is an abstract class The PyTorch dataloader tutorial a dataset class tells us that we should override the following methods 13062, How does the passenger distribution vary for the three Ticket Classes 1st 2nd 3rd Pull slider to find out The colors help distinguish port of embarkation Survived 0 No 1 9787, Checking for correlation with a heatmap is a good idea to visualize relationships 29827, Deep Learning Models 13063, Can we visualize the data in 3d This would make more sense with other types of data but we can try with Titanic passengers Try moving the 3d plot around with your mouse 11924, Our random forest model predicts as good as it did before 6293, Linear 18999, Use this instead if you want to make use of the internal categorical feature treatment in lightgbm 16286, Last 5 trees 36588, Use all training data learning rate 23168, Data Transformation 13141, Feature Engg 10266, Observations 35488, Model 40013, Insights 38407, Seems like it s NINE 28645, PoolArea 19427, Final step training 21852, How the Model Works 31213, Numerical Columns 41492, Display the classification report 31701, Checking New Features 32418, Visualize Convolutions 26052, We finally define our training loop 4707, Here the features coefficients 26257, Defining the Image generator function 1167, check some points individually to figure out the best imputation strategy 15034, Fare 25244, perform CV for catboost 26211, Here we define a nice function that is useful not only for this competition but for similar project as well 13827, Lets take help of confusion matrix to find out TP TN FP FN 29901, Linear Model 13177, let s replace na s from Embarked variables by U to indicate an unknown Port of Embarkation 11645, K Nearest Neigbors 9584, Solving linear equation with Numpy 14540, Reading and Inspection font 15394, Before we start filling the missing values for training and test data let s create an array with all the data 18982, Display more than one plot and arrange by row and columns 18355, lasso 5183, Tikhonov Regularization colloquially known as Ridge Classifier is the most commonly used regression algorithm to approximate an answer for an equation with no unique solution This type of problem is very common in machine learning tasks where the best solution must be chosen using limited data If a unique solution exists algorithm return the optimal value However if multiple solutions exist it may choose any of them Reference Brilliant org regression 7877, To solve this problem first it would be likely to think that the chance of survival could depend on the Fare 1260, Visualize some of the features we re going to train our models on 3261, strong Identifying the columns with missing values strong p 15006, A correlation matrix illustrates how variables move together 21226, Our dataset is not ordered in any meaningful way so the order can be ignored when loading our dataset 17839, We fit the model 34665, In case of monthly sales this value is of around 70 654, Before we start exploring the different models we are modifying the categorical string column types to integer 6790, FamilySize 313, Cabin 4476, Importing libraries for classification models 4513, We should impute mising MSZoning with RL 26886, Simplest approach Include only numerical columns drop all the categorical data 25776, Age 39441, Prediction Dashboard 4215, Drop the columns which have more than 70 of missing values 12837, Logistic Regression 42864, We set up utilities to encode these features using Keras Preprocessing Layers 5671, Divide Fare for those sharing the same Ticket 22939, we take a look at tickets 3885, Ordinal Attributes 35102, Run a quick visual test 15223, Categorical EDA 18529, investigate for errors 3155, I highlight the following because it taught me something I should have started my own analysis by checking for points that could be overly influential and possibly getting rid of them 34079, Ticket 31726, Anatomy and Diagnosis 26382, Initialize the weights of our NN to random numbers 20, GridSearchCV Linear SVR 25901, Tfidf Vectorization 7328, Create a feature Title 2798, Save Load Trained Model from cloud 15462, Ticket Number Cluster 40414, Created 2386, Plot confusion matrix new in sklearn 24430, Splitting into to test and train data 32579, test the objective function with the domain to make sure it works 2677, ANOVA for Classification 26543, Define Optimizer 6004, Target kita memiliki skew positif dan kita ingin mengubah target menjadi berdistribusi normal 5937, We have fill numerical columns misssing values with median 18245, View the shape 34480, Individual series weights 7708, Selecting numerical and categorical features 18335, GrLivArea 6321, Decision Tree 9121, I think I like these transformations 5680, Create the DataFrames to fill missing Age values 25674, Predictions 30719, Define the model 11037, Lets tabulate all the confidence scores and check which algorithm is the best fit for this problem 13677, Sex 19273, Loading data 6057, Heating Electricity and Air conditioning 15822, Correlation matrix 28355, Merging the bureau dataset along with application train dataset to do more analysis 116, Creating dummy variables 22178, build the final model and get the predictions on the test set 35403, Normalising datasets 23321, Mean encodings for shop item pairs Mean encoding doesnt work for me 42236, Distributions of attributes 22365, Handle missing data 2649, step is Feature Engineering 13, Modeling Evaluation 38006, This store have 3 categories foods hobbies and households which have 2 3 departments 8248, Original SalePrice Visualization 42646, These texts should be marked as 1 3005, strong Winsorization Method Percentile Capping strong font div 42400, Yearly Cycle Decompose of CA 1 store 20974, Convert Numpy Array format to Pandas Series and then to CSV format 32777, Check Score 9133, Functionality 996, How about plotting our handy correlation matrix again 3373, Importing from GCS to AutoML 13951, Outlier Detection 36877, Keras CNN model 2 4559, Dealing with features containing Years 29073, Categorical features one hot encoding 1359, In machine learning naive Ba classifiers are a family of simple probabilistic classifiers based on applying Ba theorem with strong independence assumptions between the features 20567, Analysing data with graphs 12746, As for the Cabin it s too much to fill so we can just remove them all 40265, Ground Living Area vs Total Basement Surface Area 37394, Tastes like skewness 22745, Deployment 17632, SibSp Parch 21476, All the tweets more than 150 characters long are generated We drop those since they be a later problem 554, SVC GridSearchCV 22595, Aggregate train set by shop item pairs to calculate target aggreagates then target value 15310, First we start by checking the counts of survived 1 and dead 0 17418, lets handel the missing missing values 24552, let s plot which products were chosen along with the dominant product when the customer bought only two products in any single month 33469, Date Day Week Month Year Seasonality 29464, Data preprocessing 23756, Role of Temperature and Climate in spread of COVID 19 6445, Extracting the categorical column from train and test data 8468, Univariate feature selection 8446, One Hot Encode Categorical Features 27194, Providing the Data 18294, We create Synonyms for the most frequent words 1070, I want you to look at this thread and answers 39589post222573 38129, Lets checkout SibSp feature 8724, Sale Price 18703, Re label Mis labeled images cat as dog or dog as cat 39308, XGBRegressor validation 20228, We can extract the titles from names 4414, More Data Prep 10130, Random Forest 32144, How to compute the min by max for each row for a numpy array 2d 270, It is a categorisation algorithm attempts to operate on database records particularly transactional records or records including certain numbers of fields or items It is mainly used for sorting large amounts of data Sorting data often occurs because of association rules 11149, PoolQC data description says NA means No Pool That make sense given the huge ratio of missing value 99 and majority of houses have no Pool at all in general 40930, Optimizer and Loss Functions 6825, Systematically Missing Data 22133, CHECKING FOR CORRELATED FEATURES 31208, XGBClassifier 26958, Information 37467, Necessary Data 41404, NAME HOUSING TYPE 31088, GarageYrBlt font 27972, hist 8521, Other Models 34679, Revenues distribution by category 9618, Categorical Data 5426, Since None is the most frequent value I impute None for the Type and for the area 217, As Classifier 28425, shop id 24791, Predict 17782, We replace the predicted value in the original data 24580, Check what train data looks like 32590, Steps 34, Visualizations 24102, Handling the null values 35787, add the previous averaged models here 27348, There is a clear trend and seasonality in the data let s look in more details by making decomposition 7746, we need some feature engineering 11630, Try various machine learning algorithm 13609, let s create a map that maps each value to its frequency 7730, I found these features might look better without 0 data 17684, FARE SURVIVAL 18963, Display the variability of data and used on graphs to indicate the error 22122, Advanced Ensemble Learning 13154, have a quick overview of the train copy and test copy 36813, Relation Extraction 22798, also look at the entire metrics including the inter quartile range mean standard deviation for all the features 32548, Split into train validation set 23261, Continuous Continuous 14620, after transformation you are not yet finished with the fare Normalize the feature to zero mean and unit variance using your own method or the StandardScaler of scikit learn as you have done with the ages 26576, Flow from directory 29940, The learning rate again appears to be moderately correlated with the score 32499, Loading the weights of Benchmark model 24247, Age 19276, Here I dump out the contents of the array encoding the first image in the training set 20579, Applying Feature Scaling on training data 25759, Seems to work 19325, Visulaize a single digit with an array 27966, Data Collection 27476, Each data point consists of 784 values 1924, All garage related features are missing values in same rows 13370, check the percentage of survival for males and females separately 28432, Category info 37094, Any duplicated rows 24517, There are two columns with almost all values are missing 4810, Creating a dataset which would be submitted for evaluation 18104, To evaluate our performance we predict values from the generator and round them of to the nearest integer to get valid predictions 6917, Hyper params optimization of the models with Grid Search 37793, Model Scoring on Test set 2929, RandomForest 6652, Family Size Computation 2815, train cats is a function in the fastai library that convert strings to pandas categories 36196, I would add 3 level seniority column new first 6 months 1 year 6 12 months and older 12 months and review products based on this more detail seniority But first let s decide what to do with missing data in fecha alta 25291, Prediction 31899, VISUALIZATION OF THE DATASET 36673, There are a lot of arguments and parameters that can be passed to the CountVectorizer In this case we just specify the analyzer to be our own previously defined function 8848, Numericalise 299, train and target 14242, Cabin Feature 32923, Features selection 26907, Conclusion 14907, Embarked 39980, Sice the tail is on right side the distribution is positively skewd 42229, Submission and conclusion 28749, Fit the model 21391, Feature Engineering and Time Series Batch Creation by Country Region to train them seperately as trend is very different in different Regions 6983, We analyze each discrete variable individually and make decisions based on correlation with SalePrice lack of information in each category and so on 3201, only the ones that work from the most relevant to the least one 22818, Another outlier This time with item price 13381, again check for missing values 29914, Linear Regression of Scores versus Iteration 30398, Predictions 16483, Sibling and Spouse on board 793, Visualising processed data 41249, EDA FEATURE ENGINEERING 32716, Comparing Accuracies 38429, Improving the CNN architecture vol 3 30406, Confusion matrix 29816, Vector Averaging With Glove 6774, Data Dictionary 13323, Observations 15268, Import Necessary Libraries 31009, Lets add the 2nd layer but this time we increase the feature maps 8033, Embarked 27424, Count of LB Submissions that improved score 28023, EMBEDDING METHODS WORD2VEC 7313, Model and Predict 42052, Drop columns by label names 38241, EDA 11425, Imputation and Outliers 14373, Passengers Embarked at Cherbourg having highest Survival rate 32523, Generating csv file for submission 22656, Submission 36252, Visualizing features 32467, Longevity Model 14351, Male and Female Distribution on the ship 36772, Below I have displayed some correctly classified and misclassified images which just helps it visualize better 6628, It is clear from vizualisation that most of the survivors were children and women 7133, Cabin 14916, Demonstrate all sorts of title 4447, Split to train and test data 22613, make prediction 15079, Machine Learning 40305, Question1 Frequency 40787, Visualizing and Analysis 41485, Fit the training data and create the decision trees 30989, Sequence of Search Values 33508, China Hubei 16760, HOw do we fill in the missing age lets investigate 38769, Deep Netural Network Modeling 39314, XGBRegressor training for predictions 31080, Looking at the Kde Plot and Description of LotFrontage we can replace Nan Values of this column either by Mean or Median Because data is almost Normal distribution 21616, Sampling with pandas with replacement and weights 11239, use Neural Network to do the train and prediton to compare with RF 19832, To visualise the distribution of the variable Age in this case we plot a histogram to visualise a bell shape and the Q Qplot 659, Logistic Regression again this time with only the selected columns 32801, Logistic Regression 11345, Data Preparation 1099, Extract ticket class from ticket number 21447, You can judge there is an extlier by count 14583, There are no missing values in the Age columns 3598, LightGBM 20686, We can then connect this to an output layer in the same manner 7289, LogisiticRegression Model 7102, we could draw a corrlelation heatmap 11550, Scatterplots to Explore the Dependence of SalePrice on Numerical Features 19408, Lets Predict 12832, Correlation 8901, Gradient Boosting 40067, Data Processing Feature Engineer 9379, check in test data 17647, Support Vector Machine 31995, Produce an equivalent csv DataFrame to output with the train ground truth data 41160, FEATURE 6 11541, Logistic Regression 41288, Saving the model s weights as outputs you can then download these weights later or use them as inputs to other kernels 9026, For basement exposure I just set it to the average value based on both the training and test set where HasBasement is true 19167, XGboost regressor 19075, It is clear that the no of people survived is less than the number of people who died 6663, RBF SVM 21162, loading data 27136, Above Ground Living Area sq ft is having a positive correlation of with Sale Price which is obvious 13076, We do not need Cabin and Ticket and hence can be dropped from our DataFrame 37816, look at the top 10 keywords 32304, I have created another column feature for number of people in the family by adding SibSp Parch and 1 33359, Question 3 Create a treemap plot for item category and the total combined sales 10594, Decomposition with Principal Component Analysis and gradient boosting 23546, Data augmentation 16612, Feature Cabin 24318, we filling in other missing values according to data description 32810, Level 2 Logistic Regression 38318, Improve the model 36541, At a first glance this looks similar to train except from the missing target 1346, drop Parch SibSp and FamilySize features in favor of IsAlone 16457, Feature Engineering 10941, Check the summary of test data 38369, Modeling font 2238, Transforming Missing Values 21140, Before we start antyhing we have to split our data into two parts 7381, Merging the unmatched passengers manually 18125, XGBoost is short for Extreme Gradient Boosting and is popular algorithm on Kaggle 10208, After importing the library let s check how many rows are present in Train and Test set 18706, We can now download the data frame 2019, Checking performance of base models by evaluating the cross validation RMSLE error 25642, Preliminary investigation 25426, Skin Cancer MNIST HAM10000 Repeated 15489, Train model 8416, Slope of property and Lot area 37289, Running Cross Validation 13722, Decode Pclass 18832, LightGBM 43325, Data Augmentation 40289, Ok 6653, Benefits of Feature Selection 30185, Use stratifiedshufflesplit as there exists subspecies in the image set 6162, mod catboost 1 38371, Time them 40312, Data Loading 13031, Embarked 15533, Normalize data to be between 0 and 1 25041, A right tailed distribution with the maximum value at 5 24534, Number of products by customer regularity 27315, Predictions 42574, I removed the contribution of all 0 21779, For easier grouping I would change the grouping order a little 5237, Merging Train and Test Sets 676, Final validation with the testing data set 9827, Embarked Port of Embarkation 20391, Logistic Regression Model 23089, Variable Description and Identification 8696, Similar inferences can be drawn from other plots and graphs 24971, Helper functions params 15687, Filling missing values 34637, Decision Tree Regression 9487, Predict it 32060, NOTE Both Backward Feature Elimination and Forward Feature Selection are time consuming and computationally expensive They are practically only used on datasets that have a small number of input variables 43327, Build CNN Model 20745, GarageQual column 37477, Bag of words 31381, Augmentation with ImgAug 38085, We have actually classes named 0 to 9 29477, Splitting into train and test set with 70 30 ratio 41821, In order to make the optimizer converge faster and closest to the global minimum of the loss function i used an annealing method of the learning rate 41352, Again condition is not clearly correlated as quality do but yet there is a considerable relation 4803, We one hot encode the dataset and then split it to the orginal train set and the test set 42243, Exploring categorical columns 33738, Lets load the test data 13547, PClass 30679, We make a special cross validation function for catboost classifier 35822, And now we embed each chunk individually 9591, Crosstab 8000, Regularized Linear Models 30624, Age is a numerical variable 6602, Create csv to upload to Kaggle 30312, Ensure determinism 6735, 1stFlrSF Vs SalePrice 12644, Importing 19765, The term document matrix looks like this 32221, Use month 34 as validation for training 28213, Output 18287, To get the optimal parameters I ran the code below to obtain the values to plug into the XBGRegressor 39018, Display death by age group 38818, Get the final dataset X and labels Y 7559, we have to import test file and process it before prediction 833, List of all features with strong correlation to SalePrice Log 9147, Since there is only 1 null row with BsmtFinType2 and BsmtFinType2 is highly correlated with BsmtFinSF2 I got the BsmtFinSF2 value of the null row 32982, Linear regression 34012, windspeed 37375, Decision tree 4751, For this Using logarithm transformation 13685, People with 0 2 SibSp have a higher chance of survival 40035, Weighted cross entropy loss 34699, Lagging revenues 41458, Plot a normalized cross tab for Sex Val and Survived 24021, We have almost 3 years of transactions from several 1C shops aggregated daily 9068, Observations 1806, No or Little multicollinearity 4019, Since most of the houses have MasVnrType values None let us replace remaining 8 values with None type and corresponding MasVnrArea value of 0 40030, This is the myterious unknown group of test images that holds 15 of the test data Keep them in mind 22809, Unclassified 27124, Basement 31634, Address 143, look at the feature importance from decision tree grid 37753, Technique 5 Skip No of Rows 16138, Bar Chart for Categorical Features 13579, Dropping unecessary features 25358, Create statistics from texts 17052, SVC 5185, ExtraTreesClassifier implements a meta estimator that fits a number of randomized decision trees a k a extra trees on various sub samples of the dataset and uses averaging to improve the predictive accuracy and control over fitting The default values for the parameters controlling the size of the trees e g max depth min samples leaf etc lead to fully grown and unpruned trees which can potentially be very large on some data sets To reduce memory consumption the complexity and size of the trees should be controlled by setting those parameter values Reference sklearn documentation learn org stable modules generated sklearn ensemble ExtraTreesClassifier html 14933, Sensitivity P hat Y 1 Y 1 frac a a b 10772, Pre processing Three 39031, Hyperparameter 24038, Encoding data for random forest and knn 37822, Remove punctuations special characters numbers 3882, We now train the pipeline with 70 of train data and predict the prices of validation dataset 30 of the train data we set aside 1821, Phew This looks like something we can work with find out the MSE for the regression line as well 32700, n estimators the number of decision trees in a forest 42862, Prediction and submission 20347, The fast ai way 16400, But other than that pd 34363, Checking for missing data 37474, Lemmatization is similar to stemming but instead of looking for a stem of a word you look for its lemma 11075, Ensembling and Stacking 24365, There are some variations in the median price with respect to time 19842, As we suspected there were very few children at the age of 10 on the Titanic 2256, Age and Sex 12482, Embarked Title Pclass 28574, BsmtFinSF2 39858, Training 15507, Fare 42645, Mislabeled Samples punctuations and stopwords were removed 29221, Looking good now we try to handle the 116 categorical variables 18541, here a lot of non normal distributions 10538, Get Dummies 8620, OveralQual vs SalePrice 27633, Lets do an initial top level correlation matrix analysis 38223, Dates Day of the week 8536, Pareto Approach 612, let s check what s going on between Age and Embarked 30187, remove index False from to csv 15532, Convert data to categorical variables 14371, Feature Description 28236, Define the optimizer 30610, Examining the feature improtances it looks as if a few of the feature we constructed are among the most important 584, Data processing 258, Fitting Model 4348, If the skewness is between and the data are fairly symmetrical 10322, take a look to the kitchen sink regression for the full training set 7067, GridSearch for Light GBM 4640, Number of Missing Values in that variable for all the rows 17891, Some ticket numbers have alpha charaters in the number 42233, Categorical columns within the dataset 7507, Feature engineering 38476, Axis 1 and 2 As Feature 12120, PoolQC data description says NA means No Pool 37045, The process of converting data to something a computer can understand is referred to as pre processing One of the major forms of pre processing is to filter out useless data In natural language processing useless words data are referred to as stop words 5391, How about feature importance 42120, we ll train on the full set and make predictions based on that 16000, Age 22805, Exploratory Data Analysis 34515, Bureau Balance 12066, Correlation with Target 227, Library and Data 10139, Data Model Selection 43268, Importando a classe RandomForestRegressor 28950, We predict the values of holdout set by training the model on the entire train dataset 8819, FEATURE ENGINEERING 36279, Feature Engineering 36854, Reading the data 7023, Heating quality and condition 29019, Pclass 420, From this we can tell which features OverallQual GrLivArea and TotalBsmtSF are highly positively correlated with the SalePrice 43326, let s take a look on our Augmentation datagen 6309, Logistic Regression 37743, We should stick to using the category type primarily for object columns where less than 50 of the values are unique If all of the values in a column are unique the category type end up using more memory That s because the column is storing all of the raw string values in addition to the integer category codes You can read more about the limitations of the category type in the pandas documentation 21623, New aggregation function last 16471, Reasons to keep linear model and MLP 30393, Embeddings 20965, Initialising the ANN Model 26051, we ll define a function to calculate the accuracy of our model 25198, Callback Technique 29045, Resizing the image 32803, XGB 26664, bureau 239, Model and Accuracy 2316, Method 2 With Patsy using R like formulas to split the dataframe 2368, Supervised Learning Stacker 34875, Nice Another 3 increase 6503, Read Data 25248, Base XGBoost Model 20458, Number of children 24385, Where do we have NaN values 41337, Categorical Variable 32746, The Random Forest Classifiers for 8 options of selected feature sets 19895, Several shops is duplicated which can be determine by shop name 7149, F LTER NG DATA FRAMES 42776, Random Forest 29905, Fully Connected Model 32843, Training Function 3512, Make predictions on the test set and write to csv 28135, Compiling our model 33516, China Hubei 33687, Weekend or not 8467, Sequential feature selection 8028, Fare 27189, Initial Feature Analysis 18604, Looking at pictures again 11952, Finding best parameters for each model 16607, Categorical Variable 23920, Simple transformers 26976, Prediction 14927, K Nearest Neighbor Classifier 40325, Users 23576, Instantiate the tuner to perform hypertuning 3954, Create TotalBath Feature 17954, Gradient Boosting Classifier 17551, Replace the missing values of Age column with entries with similar other parameters Else replace with mean age of dataset 2745, One way to handle missing values is to drop them 16497, Decision Tree 39882, Prediction with RandomForestRegressor 32127, How to convert a numeric to a categorical text array 6856, Pairplots 4126, Stacking averaged Models Class 14149, First we explore the size of data that we have 10224, Before start with modeling let s check with missing values in training data set columns 1847, Distribution of Continuous Variables and Effect on SalePrice 40397, Fold 4 37308, Download submission csv file 21449, Datetime 33564, Submission 17596, now stepping onto the second task that is creating a new column and creating catergorical fetures 19975, we get val loss and val acc only when you pass the paramter validation data 15548, Filling 22713, Creating list of labels for the plot 31814, Generate the ground truth anchors 2811, Dendogram 9320, Another approach can be to first create the dummies and then split into training and validation set In this case we get 24829, chi2 6487, Houses with central air conditioning cost more 8356, We group the roalties and assign masters to Mr and due to the fact that there were not so many roaly women we assign then to Mrs 5805, Thats default parameters 16, GridSearchCV Lasso 34025, Delete Atemp column 16605, Analyse the distributions of continuous variables 20132, Judging by the plot the best value for C is somewhere between 0 13853, Categorical features 8745, Skew 6589, We can compute the score of each feature to drop any unwanted features 36878, Predictions for test data 37915, Prediction 7626, AdaBoostRegressor 4918, Handle the Categorical Features 39406, Exploring the data 8799, Removing the highly correlated variables 1314, Feature Creation 20160, Splitting data into Train and Test Data and Labels 3395, Missing data 10566, Our task given a user we predict and return a list of movies recommendation for that user to watch 32677, Blending proved extremely helpful on the enhancement of error metrics in this exercise 35892, Calculate derivatives and fit model 1600, Target Distribution 22931, Another interesting feature that I noticed in the name feature was the presence of a second name which was denotated by the brackets 11442, 128233 Importing the Libraries 43053, check the distribution of target value in train dataset 37765, Technique 10 Memory leaks 34849, Variable Correlation 7493, Weigh the embarked feature with the survival rate into a new column 31583, let s check how the target classes are distributd among the REG continuous features 5126, ExtraTrees Classifier 36895, from submission 14203, Checking with some k Fold cross validation 24053, Removing outliers and imputing missing values 29935, Function for 3D plotting 16019, Family feature makes difference more obvious 14879, Import Data and Package 7054, Garage type 40693, note that this way this is hard to visualze a better way is to convert the temp variable into intervals or so called bins and then treat it like a discrete variable 16937, Sklearn 11279, We can use the Series 21500, Plot pie chart with percentage of images of each diabetic retinopathy severity condition 10456, Filling in Missing Data 6670, Cross Validation 41056, Interesting and plotting the most extreme contributors to the first principle component char 38 isn t among them 39353, Submission 39121, Decision Tree 3685, Transform variables 10151, Contents 24443, ul style list style type square 26687, OVER EXPECT CREDIT actual credit larger than goods price 33995, Convolutional Neural Network CNN 41274, Arrow is especially effective 20636, N gram Analysis 1373, Looking the Fare distribuition to survivors and not survivors 23716, Now we check skewness of the selected variables if any variable is deviating from normality 16104, Age Feature 8739, Total Basement Square Footage 704, First steps create a copy of the data and turn the categorical data into dummy variables 13753, Plot categorical features 41987, isnull any isna any To check if there are any null values empty or NaN Not a Number 37341, VGG16 is a convolutional neural network model proposed by K 5518, Gradient Boosting Classifier 19572, Inference 32545, Converting Categorical to Numerical 40880, Predictions look pretty similar for all the 8 models We would like to take kernel ridge svm lgb gb and ridge as the base models for averaging ensemble method you might wonder why I don t choose lasso and elastic net instead of gb and lgb since the former two are superior in terms of rmse As I said earlier the more diverse our base models are the more superior our ensemble is We saw GrLivArea is the top priority for all the 6 models in feature importance section But the second priority for lasso ridge and elastic net was YearBuilt while it was LotArea for xgb gb and lgb That s the variation we need for our ensemble to get better at prediction If we would choose lasso and elastic net there would be similarity instead of diversity well that s just one example there are many more our ensemble would not perform according to our expectation I encourage you to experiment in this part 34689, Average USD RUB exchange rate 7785, value counts with bins 7540, Two missing values belong to same Pclass and Same Sex with same Fare category ie g Lets explore further more 20397, Voting Classifier Model 1382, we might have information enough to think about the model structure 12977, We have usen Name variable to create new feature as Title Therefore we donot need variable Name anymore and we drop it from the data 14549, Survival Rates Based on Gender and Class font 1115, Age Missing Values 20819, We can use head to get a quick look at the contents of each table 14726, Preparing the Data for a Machine Learning Model and Feature Selection 26320, LightGBM 6943, Visualization 9732, I prepare the dataset for training by separating the target variable LotFrontage from the rest selecting relevant features and dummifying categorical variables 5621, Using pandas DataFrame replace 5668, Last Name 30535, Exploration of POS CASH Balance Data 4281, The Sale Price Histogram is right skewed ranging from 34 900 USD to 755 000 USD 20823, join df is a function for joining tables on specific fields 6575, As there is 681 unique count we drop ticket feature from our dataset 18034, Woman 31944, Try using Label Encoder 5541, Submit prediction 23573, It s not quite gaussian but we might expect that because the number of samples is very small 37890, Top 10 Feature Importance Positive and Negative Role 5312, Final feature selection 35552, Combining the meta features and the validation set a logistic regression model is built to make predictions on the test set 25468, Update parameters 32149, How to compute the euclidean distance between two arrays 17608, Decision Tree 12030, From the makers of bamboolib 40006, In contrast we can find multiple images for one patient 1267, Submit predictions 19234, Keras example 23714, we combine all the features to store it in df train DataFrame 17739, simply having a cabin number recorded gives you a survival advantage 25383, Visualize the model 246, Library and Data 38757, Linear Discriminant Analysis LDA 29527, Logistic Regression 2799, datasist 33756, Make predictions 40766, Optional steps my way 22099, Validation Test Accuracy 25482, Evaluate the model 40834, that we have got our guns lock and loaded it s time to shoot 36808, Lastly we achieve our final goal entity extraction 29315, The younger you are the more likely to survive 16629, Trained Model On Whole Data 39342, Prepare the dataset 13321, Observations 1047, We do the same thing with SalePrice column we localize those outliers and make sure they are the right outliers to remove 10801, recall C Cherbourg Q Queenstown S Southampton 50, Linear Regression 35340, PROPERLY CLASSIFIED IMAGES 24803, Retrieving predictions 17994, Often the tickets are shared among family members with the same last name 4681, At first glance it appears that we have a lot of work to deal with missing values 32324, cleaning 26062, The data we want to visualize is in ten dimensions and 100 dimensions 26243, Training 32512, Generating csv file for submission 29143, Plot ly Scatter Plot of feature importances 15126, Engineering 24319, And there is no missing data except for the value we want to predict 25347, Our model 38265, First we fill all the null values with no column name 24368, Total area in square meters 35816, Target encoding 32565, Diff Patient id 896, for SVM classifier 39096, The Fog Scale Gunning FOG Formula 25267, Adding image type with image in dataframe 23190, Looks like gbc is better than rf in terms of f1 score 24497, Zip the inputs together so that the network uses the whole set of X y values for learning and prepare the test data as well as sample submission data 14898, Age and Family size vs Survive 39726, to look at what we ve made 33640, Bivariate Analyis 14770, Ensemble 7960, In our data set we have 80 features check their names and data types using the dataframe attributes columns and info 34692, Average number of items bought 29580, A new transform we use is RandomHorizontalFlip This with a probability of as specified flips the image horizontally an image of a horse facing to the right be flipped so it face to the left We couldn t do this in the MNIST dataset as we are not expecting our test set to contain any flipped digits however natural images such as those in the CIFAR dataset can potentially be flipped as they still make visual sense 23696, Evaluation 16856, AGE 836, List of features used for the Regressors in 24098, Boxplot for finding outliers 6032, Check Correlation between features and remove features with high correlations 3177, Not bad of a timming 29754, We process both the train data and the test data 16597, Submit the file to competition 35599, Using XGBRegressor to calculate the housing price 40044, now we have created a hold out dataset that only consists of one type of image group 31328, Prediction 43262, Selecionando as Colunas que Iremos Executar o Modelo 2324, Grid Search Randomized Search the quest for hyperparameters 36871, Keras 1 hidden layer 21936, Spark 7460, We can use the pandas 15661, Support Vector Machines 28401, Filling NULL Values with KNN 2376, Four ways of displaying the model coefficients 41377, Numerical Features 24579, Load Data 10199, we have added some more high correlated columns to the dataset Lets continue 8049, concat train and test 36714, Large 25243, Add aisle and dept id for catboost 21585, Select multiple slices of columns from a df 13156, Since Embarked was such a complicated feature to get a trend of we ll simply use OHE on it and let the model decide the trend itself on basis of other features 19539, Creating double exponential filtering over the time series 4204, Any duplicated rows 2489, so here we are using the Regex A Za z what it does is it looks for strings which lie between A Z or a z and followed by a dot we successfully extract the Initials from the Name 35074, The network on Solution 5 threw actually a worst score than Solution 4 even though I added Ridge regression to the first layer 6150, Looking for the best hyperparameters 2241, Categorical Encoding Class 33099, LightGradientBoosting regressor 22131, CatBoost 20456, Flag own car and flag own real estate 22619, Plot the last 25 of the devices 11505, Modeling 4057, The transformer missing data fill the Age rows with missing data considering the procedure described in section 2 if the person is a Master we ll input the masters average age and the non masters average age be placed in the othwe missing age rows 22539, Put it all together 24502, Fill in the submissions table 33815, Testing Domain Features 4133, One Hot Encoding rest of the features 41673, let s check what we have to predict 37738, Comparing String to Numeric storage 2828, The below parameters come from this kernel we can optimizing hyperparameter but it take a long time a lot of processing power catBoost parameter tuning 12209, And there we have it some categories converted into numeric features other first recoded and the dummified 20750, EnclosedPorch column 11820, SalePrice vs Total Basement Area 38486, History of CNN font 42799, Missing data 14653, Dropping Columns 7764, Ridge Regression 12980, Embarked 20164, Case 2 Binary Images 17681, GENDER WISE SURVIVAL PERCENTAGE 23829, Again setting a threshold of 0 21178, confusion matrix 36806, Going along the process of named entity extraction we begin by segmenting the text e splitting it into a list of sentences 37939, The most Covid 19 cases are by far in the United States 28207, First we need clean out data by convert text columns and removing irrelevant columns 7964, After reading the description there is the way we impute and cleaning the missing values 35763, now we have 100x the original number of features 23475, Processing of the test file 13516, Feature Engineering 41598, Compile the model 29989, The following function returns a single image from the hdf5 file 11066, Title feature 22246, Class 8946, Fixing Basement 20681, Make a Prediction 18300, let s remove outliers over the 99th percentile 14746, Model 3 2780, Import libraries Load dataset 15872, Hyperparameter tuning 28083, The following is the method to read csv file in Spark 10543, Splitting data into training and testing data 5967, fare feature 20434, Loading tokenizer from the bert layer 30606, Fortunately once we have taken the time to write a function using it is simple 2616, Library and Data 14810, Correlation Between Sibsp Parch Age Fare Survived 20318, Section 2 Processing and viewing our Data 10754, select those passengers who embarked in Cherbourg Embarked C and paid 200 pounds for their ticker fare 200 11190, Kernel Ridge Regression 5589, Discretize Fare 21363, Training on both targets MULTI TASK LEARNING 9951, Violin Plots 4673, Removing outliers 13334, we want to visualize every possible Title value and correlate them with the Sex column 3819, Definition Confidence intervals of sample means can give us information on the population mean of data when we acquire a single sample of data and know the population standard deviation but not the corresponding mean We take our single sample mean and use it to get a range of values with the following formula Confidence interval bounds sample mean z star standard error We already know the formula for standard error from before and we get z star by locating the corresponding z value using the z score table of probability halfway between our desired confidence level and to account for both tails of the normal distribution For example if we want a confidence interval which is standard we locate the z value corresponding to which is With the two bounds we get from the formula we can be or any desired confidence level confident that the true population mean lies between the two values 22344, TF IDF Vectorization 22226, Python Keras K t phanesiyle N ral A olu turma Create Neural Network with Keras in Python 30634, Relationship between embarked and survival rate 28069, we have a trimmed dataframe 21841, RNN with 1 Layer and Multiple Neurons 12705, Sex 25192, Reshaping our data 33339, Quick look at sales df 3926, Naive Bayes Classifier 6718, Find Outliers in numerical features 20688, How to Develop Deep Learning Models 33151, Train a model using Keras 26395, It is important to find out how the different features correlate with Survived 15164, Get Rid of Redundant Data 10044, Split data set 24237, Generate csv for submission 24307, Model Evaluation 2267, Name 7284, Random Forest 15878, But let s take a look at all of the models so we can make a more informed decision 3979, Read data 16644, KNN 40960, LabelEncoding the categorical values to change them to int 4327, SibSp and Parch combined 30777, Find the score of cross validation 5018, Outliers 16914, Correlation Evaluation 7137, AgeGroup 5338, Diplay series with high low open and close points Similar to candlestick 28637, GarageQual 10506, Fare 3553, We talk about the Fare now but let s take a look at the correlation matrix first 14291, Creating submission file 16247, Imports 727, We ve addressed a lot of the issues holding us back when using a linear model 26209, Most people use df width unique df height unique 1024 to check if all images are of 1024x1024 resolution But we not be 100 sure if its true in the training folder we won t use the same way here 25897, Latent Dirichlet Allocation LDA 37357, Embarked vs Survived 7573, Visualizations for numerical features 34410, Filter out rows with incomplete data 32709, Splitting Data into training and validation data set 6771, Import required modules 37073, Encoding Categorical Variables 10723, Handling missing values 24767, LightGBM 3156, And then there s more data cleaning 441, Electrical KitchenQual Exterior1st Exterior2nd SaleType Since this all are categorical values so its better to replace nan values with the most used keyword 41127, Additive Model 25902, Building basic Logistic regression model 10399, Blending with top kernels 5140, Outliers 32093, How to replace items that satisfy a condition without affecting the original array 8516, Modeling 8785, Tunning Params 27545, Display heatmap by count for multiple categories 36114, Lets have a look to our new cleaned data 16184, We can now remove the AgeBand feature 38707, Selecting Features 15357, SHAP values 42989, Visualization 23250, We want to give numerical values to our model we convert the object type values to numeric values 11287, Categorical Features replace missing values with None 42090, Apply Model to the Competition Data 12253, Data 11458, Embarked Pclass and Sex 23469, Using different models for casual 22130, LightGBM 38135, encode Embarked which is of type object 11713, Decision Tree 43162, Submission 20298, Survival for alone passenger is very low and families with member 4 decreases 38475, Axis 2 As Feature 25842, Getting textfile 32025, In the same manner convert Embarked column of test X 38543, F1 looks good But sometimes it may not 8008, Boost Decison Tree 20624, write a small function to draw graphs for stopwords and punctuations present in the tweets 18154, Comparing the model and choosing best model for prediction 1563, Ticket 37455, This is a multi input model 24756, Get dummies 37313, XGBoost 12444, Final predictions 35577, Sales Heatmap Calendar 2134, Tuning RandomForest 9437, kdeplot 39223, Duplicated Features 14852, Parch 8820, Feature Engineering Name Title 10118, No surprise first class passengers have survived more than the rest 36774, MISCLASSIFIED IMAGES 1156, Background 29539, we re going to train our model import some libraries 25944, Final Fit Predict 27440, Model 5696, Features engineering 42031, cut to change continous values to ordinal groups based on physical numbers 4345, Actually MSSubClass feature is a categorical feature 13559, Fare mean by Sex 36378, Reshaping 17643, Fare per person Age are important because as they have float datatype we cannot use them for groupby 9294, Regression on survival on Fare span 22524, Visualise comparison of model performance 35934, Cabin 20404, Plot the 20 nearest word embeddings using a distinct color per cluster 35862, And WHen you check out the training and validation set combined 25679, Rebalancing the Data 39022, Correlation Heatmap 19260, Drop rows with different item or store values than the shifted columns 1415, Title vs Survived 23713, Extensive Feature Importance Visualization 16692, Feature engineering 2886, two categorical features we are going to exxplore is OverallQual and OverallCond 42943, Modeling 5420, In my opinion I think since the distribution survived and dead is pretty clear 8529, Masonry Features 14789, Family Size SibSP Parch 42612, Preprocessing the data 16723, embarked 31678, Defining a function to plot the images of numbers 13220, Building Machine learning Models 20135, check if there is any imbalanced in data labels 10600, Step 3 Clean Your Data 40413, The longitude values range between and the data corresponds to the New York City 14128, Observations 400, Gradient Boosting Classifier 42684, Before starting EDA It would be useful to make meta dataframe which include the information of dtype level response rate and role of each features 13980, The passengers with title Mr are more 15140, Cleaning 19005, Run this cell to do HP optimisation 35852, we reshape the actual test and train set for the conv layers 19905, Grouping by month and shop Id only 36562, Hyperparameter Tuning 41588, Visualizing Predictons on the Validation Set 35581, Preparing Dictionary 43144, Making predictions 6743, KitchenAbvGr Vs SalePrice 21056, Validation Set 30918, Measure F1 for Validation data 16369, Binning The Age 42082, We freeze the base layer ie its weights are not going to be retraning the model 15742, MACHINE LEARNING 28490, By checking the maximum and minimum values of these columns we can make sure which ones to convert to type int32 25455, Using the bottleneck features of a pre trained network 12124, FireplaceQu data description says NA means no fireplace 41027, Explore adult males 21090, One Hot Encoding 29772, We save the prediction in the output file 37406, drop the columns one hot encode the dataframes and then align the columns of the dataframes 2121, then create a pipeline for this model 20127, I can build a feature matrix where the data is all ones row ind comes from trainrow or testrow and col ind is the label encoded app id 38517, Distribution of top Trigrams 30993, The public leaderboard score is only calculated on 10 of the test data so the cross validation score might actually give us a better idea of how the model perform on the full test set 22018, saldo var30 23195, To implement an ensemble we need three basic things 7958, RMSE 0 17788, She is traveling in Cabin D17 in 1st class 12316, It is no wonder that the FullBath increases 3378, Download your predictions 10514, Convert the Categorical Variables into Numeric 18070, Compute at the number of train and test images 31935, Here are some examples of the hand drawn digits from the train dataset 31251, Feature Selection 22816, Using google translate I understood that this item is related to point of delivery and the Russian shipment company Boxberry 21741, Data Leakages 12167, Min leaf nodes 5367, MDI Feature Importance 33892, agregating credit card balance features into previous application dataset 6230, Check correlation of features 20284, Age 18038, Prepare the test data 12237, Tuples 13950, Basic Data Analysis 4269, Functional 7863, Import Data Exploratory Data Analysis 19013, Experiment 3 247, Model and Accuracy 41991, Locating loc To read items with a certain condition 2699, Based on the previous correlation heatmap GarageYrBlt is highly correlated with YearBuilt so let s replace the missing values by medians of YearBuilt 20028, Define classifier over 10 folds 4164, Fill missing data with random sample 27778, Normalization 32863, Group based features 40153, Store a unique Id for each store 16703, drop Parch SibSp 30199, Include back the id and price doc which we had removed from tempData for correct mice NA imputation 1762, Ticket 23427, we move on to class 0 30385, Model 28194, let set the stopwords for english language 13264, Cabin 15007, The correlation matrix contains a lot of information and can be difficult to interpret 22719, Plotting the animation 10722, Feature selection 3948, There are two features related to MasVnr that have missing values MasVnrType and MasVnrArea 18135, Submission 1229, remove outliers 35333, Building the ConvNet Model 13553, Looking quantiles of Fare 40080, we finalized our values with lowest Root Mean Square Error 5512, Modelling 13666, KNN 9805, Submission 28790, Most common words Sentiments Wise 37903, Prediction from Linear Model 14786, Cabin 15017, Age 9972, The Have Pool it s a boolean feature if the pool area it s greater than 0 means that house have a pool 34673, There is a clear seasonality in both overall amount of sales and average sales per day 3748, Filling the null values with mean calculated of the feature 37469, Cheat Sheet for Regular Expressions 426, Missing Data 2481, Making submission 26861, For the solution uncomment and run the cell below 37235, Submission 1930, st Floor in square feet 28120, The weekend looks noticeably different from the weekdays greater proportion of late night activity no real peak in the early afternoon 37657, Check the test image formats 11025, lets create Fare band with 4 segments 31260, Our best parameters 15594, Data Types 29564, We take log transformation of the y variable 24833, use Naive Bayes 5340, Diplay increase and decrease of counts in waterfall chart 6409, There are some Date Variables in the dataset when we performed df head check again 22771, We now build the vocabulary using only the training dataset 32084, Bivariate Analysis 40077, look for multicollinearity within 19290, UPDATE Here we are reading just the validation set 7491, Fill missing data for embarked feature 16712, Nearest Neighbours 21065, Tokenising and Stop words removal 16783, Decision Tree Classifier 4095, Fare 14468, CAUTION Make sure that PassengerId and Survived are saved as int in the submission file Otherwise the submission be accepted but scored as 30338, This function is to do tta 13831, Fare 40724, Learning Rate 29065, Geolocation features 9123, convert categorical ordinal variables to integers 43101, Model training and predictions 17771, Most of the passengers traveled alone 25014, Creating Submission 15933, Pclass 20349, Here we run the learning rate finder Per Jeremy Jeremy says lesson 3 49 00 do lr find lr plot and find the learning rate with the steepest slope not the bottom 31093, BsmtUnfSF font 7038, Type of road access 36893, Comparing classifier performance 36268, Configure the heatmap 8270, Create YrBuiltAndRemod Feature 6769, Score Summary 35928, Data skimming 3248, Scaterplot Matrix w r t Sales Price 6525, We may assume the same numeric features be skewed in test set as well 17277, Random Forest 8485, Orthogonal Matching Pursuit model OMP 25583, Utilities 9351, Predict 19693, Train Test Split 35067, Solution 4 2 Convolutional layers with 32 feature maps 9929, I wanted to study outliars in the most relevant features and that s what I did 8669, Gradient boosting with CatBoost 41030, build an ensemble 20877, Let s take a look at the training dataset 928, Optimize Elastic Net 9844, Roc curve 1261, Setup cross validation and define error metrics 39716, Once the vocabulary is created and the Word2Vec model is specified I train this model by calling train function 40147, We are dealing with time series data so it probably serve us to extract dates for further analysis 24033, it is possible to calculate deaseasonalized item sales count 4383, Something look surprise in histogram of test data 8768, Survival by number of parent or children 42173, To know the data type it contains we can run the following command 7438, As before I also expriment with original y train and log transformed y train log in the neural network model as well 13092, Correlation between Quantitative variables 29209, After making some plots we found that we have some colums with low variance so we decide to delete them 18458, Supervised encoding of location coordinates 2284, Confusion Matrix 5969, Creating dummy variables 13273, Import libraries 32379, Visual Inspection of Mysterious Image Set 26845, First batch 221, It is single layer neural network and used for classification 25188, Plotting first six training images 3067, Embarked 23569, Train the model 11615, Exploratory Data Analysis 37086, Boosting 11352, Exploratory data analysis 38177, Create a Model 33584, TTA 2852, Libraries and Data 11996, Ridge regression is a regualrized version of linear regression means it shrinks those features which are unnecessary for predictions 7833, This looks like a good feature to filter out low priced apartments 8984, This leads me to believe that I should have a column for 1946 NEWER style 7519, Partial Dependence Plots 9256, Lasso Regression 26696, Sales Data 68, Test Set 6067, PoolQC irrelevant I can drop it 53, Important features 35887, Impute numeric columns 41026, we modify the Fare column another time to create the Pfare feature that is just a passenger s Fare divided by his ticket frequency 29941, Correlation Heatmap 14922, The p value of PassengerId is larger than 0 21206, Random initialization 35155, Experiment Dropout percentage 15243, Function 18849, First we want to find the correct learning rate for this dataset problem 969, We start by loading the libraries 22899, Data Preprocessing Part 15240, Age 7512, Parametrization of the XGB model 29063, HOG 12004, Looks like that lasso performs less better than ridge as it not able to regularize well 4844, Coreect it with Box Cox method 35711, separate into lists for each data type 6665, KNN Classification 2747, The other way to handle numeric data is to fill the columns 10901, First run the algorithms with the default parameters to get an idea about their performances on our data and then we would tune the parameters of better performing algorithms to improve their performances 18740, Stopwords 3684, Missing Values 27402, Define the CNN Model 13727, Filling in the missing values in Age 36251, A simple cleaning module for feature extraction 14638, I have grouped the age by Gender Ticket class and Title to get the median age 6612, Concatenate Test and Train data to develop the categorical data 18040, Submission 28154, install the pytorch interface for BERT by Hugging Face 16722, Pclass 16685, We can visualize this data as being concentrated in different regions 28584, LowQualFinSF 23470, We can therefore conclude that the XGBoost Model works best for predicting casual 14716, DECISION TREE 13739, Random Forest Classifier 16934, All this feature preprocessing have a big impact on the model precision 20551, Distribution plot 9777, Decision Tree 19414, As stated before we be using PytorchLighning alongside the library from 10811, it is better 38727, Again this code is very similar to the previous one for the FCGAN 1897, LogisiticRegression Model 14446, go to top of section engr2 4418, Using cross val score 28620, MasVnrType 42731, Using corr and numpy boolean technique with triu we could obtain the correlation matrix without replicates 6172, Sex 26038, we use the random split function to take a random 10 of the training set to use as a validation set 748, Sumbit Code 13668, Naive Beyes 35158, Conclusion Both a 40 and a 60 dropout present the higher accuracies I choose to use a final 40 dropout 1150, TraiF Test Split 22173, create some new features from the given features 6304, Gradient Boosting Classifier 16965, The Embarked and Fare columns have 2 and 1 missing values respectively 24470, Correlation 13068, Random Forest Classifier 37805, LASSO Regression 4150, Check for missing values in age variable 41220, Just curious let s check if the unimportant feature have any pattern order 30139, Implementing Custom Model 32640, Text 31250, Family Survival 43317, try with different classification algorithms 31537, since 50 values are zero so replacing with zero 29750, Sample images 20483, Credit sum AMT CREDIT SUM 35225, How much impact does have 29368, NAIVE BAYES 29709, We would like to know which features have missing values the most 8941, Fixing LotFrontage 14639, Great now let s write a function to fill the missing values of age in the training and test set with these smarter statistics 25009, Trend Features 36302, print our optimal hyperparameters set 10260, We ll now glue the training and test set together for all but the sale price which the test set doesn t have in this case 717, lets examine the numerics which ought to be categorics 11343, Age Binning 5279, Creating the Feature Importance Dataframe 38113, Preparing prediction dataset in wide format and then evaluating it 24945, Feature selection 9, Label Encoding 18218, Agora montamos nossa rede convolucional 3793, The 2 points in the bottom right are outside of the crowd and definetly outliers 13326, Embarked completing and converting to numerical values div 12973, Pclass Parch and SibSp have obvious correlation with Age while Sex doesn t have Therefore it can be logical if we use Pclass Parch and SibSp variables for filling the missing values in Age variable 20936, Submission 39449, bureau data 18504, This submission scores on the leaderboard you can try it out for yourself by heading over to the Output tab at the top of the kernel 27490, Define a neural network model with fully connected layers 24844, V Submission pipeline fixed score 1096, Fill missing values in variables 35239, Two records were found to be null after removal of punctuations 13512, More Exploration 34327, Test if the generator generates same images with two different sizes 20374, Having invoked the t SNE algorithm by simply calling TSNE we fit the digit data to the model and reduce its dimensions with fit transform 38233, Model Evaluation and Validation 19543, Removal of Punctuatuation 3370, From there we ll use our account with the AutoML and GCS libraries to initialize the clients we can use to do the rest of our work 35052, I compiled this model using rmsprop optimization categorical crossentropy for loss measurement and accuracy as metrics measurement 7131, Embarked 32630, XGBoost 42456, ps car 03 cat and ps car 05 cat have a large proportion of records with missing values Remove these variables 26588, TASK IMPORT STORE INFORMATION DATA 22279, Feature Engineering 18562, Looks strange that there are 16 passengers with family size of 7 for example 29401, CHECK NUM OF ROOMS 36490, Keras Run Functions 16338, Decision Tree 5562, I use my own custom simpel imputer it act as simpel sklearn imputer by set strategy most frequent but on categorical data This may not best choice 25017, 3D plotting the scan 128, Misclassification Rate Misclassification Rate is the measure of how often the model is wrong 26716, Plotting boxplot for price changes 3843, Munging Age 23566, Using OverallQual for making more meaningful imputation for missing value cols 4387, create one combined feature total porch area 1974, Model evaluation 9288, Imput Missing or Zero values to the Fare variable span 38151, The heavy lifting optimizing 18512, Sorted Slices of Patient 0acbebb8d463b4b9ca88cf38431aac69 Cancer 19420, unpack this whole process data function 10099, encode the categorical values 37723, Feature Variance Analysis 40613, comes the cool part 1039, We dealt already with small missing values or values that can t be filled with 0 such as Garage year built 3020, Visualize the destributions of the numeric features 37224, There are also frequent mentions of height 8298, Extra Trees Extremely Randomized Trees Ensemble 23906, Service functions 19405, Making Prediction 16442, We fill missing values based on Pclass and SibSp 24715, the bottom rows at each column hold real leaf images that have the first PCA coefficient be at the value of the corresponding percentile of that column for example the left most bottom pictures are leafs with a PC coefficient to be approximately and the right most bottom pictures are leafs with a PC coefficient to be approximately 25449, Applying Random Foresting 17045, Modeling 4867, Finding features with NA values 12351, Remaining Basement variabes with just a few NAs 37100, Transforming 32103, How to swap two columns in a 2d numpy array 30391, Padding 28319, Exmaine the POS CASH balance DataSet 36867, Like for KNN GridSearchCV for SVM takes very long so I only fit one good set of parameters here 9880, Correlation Between Pclass Survived 573, StackingClassifier 35077, Making predictions using Solution 6 11065, Family Size feature 31805, Submissions 33436, The frequent words are similar in fake and real twitters 31641, DayOfWeek 24152, Plotting 39723, I can think of a few things we could do with the names 31472, Data Augmentation 30582, First we can calculate the value counts of each status for each loan 24789, Loss 5320, Library 33692, Days difference from next row 29799, Measure similarity b w two words 6143, We divide all dates into four bins 6486, This is a list of highly correlated features 10580, Visualizing AUC metrics 29958, Training 2001, Nice We got a 0 32510, Prediction 35328, I have created the training and validation sets Much of this section is inspired from the sentdex classification example with convnet work 28461, collect all columns of type float and missing values in a list 19052, Lets first specify the x or input 2173, Regarding family size our hypothesis is that those who travel alone have a lower survival rate 23381, I add some methods to the class that keras needs to operate the data generation 28157, input ids a sequence of integers identifying each input token to its index number in the BERT tokenizer vocabulary 61, Pytorch Loss Function Cross Entropy CE 4776, Gaussian Naive Bayes 40094, Language Features 28057, Continious variables 35684, XGBoost 36963, Testing new Parameters 26717, Calender 8048, automatic outlier detecting 6882, Missing values 21946, Transforming from spark DF to RDD 17738, most first class passengers had their cabin numbers recorded in the dataset yet only a small fraction of 2nd and 3rd class passengers had theirs recorded 1973, Gradient Boosting Classifier 4433, Feature Selection 30357, Predict by Specify Province 4709, Features importance 4536, Numpy DataFrame Series Pandas 43249, Logistic Regression 20685, a fully connected layer can be connected to the input by calling the layer and passing the input layer 36792, Lemmatization 16479, Joint plot Distribution w r t to AGE FARE 20042, Import necessary libraries 3557, We now build our pipeline of transormation that we use to get prepared data 40839, Look at another one 41848, Average price per day depending on distance to Kremlin 38924, Non CV LGBM Approach 16154, FamilySize 32384, Setting Cross Validation and Hold out Set 23955, Applying Decision tree Regressor 7784, value counts with NaN values 12919, Survived 41157, FEATURE 3 AVERAGE NUMBER OF PAST LOANS PER TYPE PER CUSTOMER 13985, There are two null values in the column Embarked 401, Logistic Regression 18378, The R square value for the test set is higher 2491, Filling NaN Ages 11204, We just average four models here ENet GBoost KRR and lasso we could easily add more models in the mix 19140, Model 2 with Adam optimizer 37763, New approach 3658, Comparing accuracy scores 40049, This way loading the images be much faster than doing this on the fly 36534, a long story it is 20555, Sequential model 21199, update the parameters 32706, Importing GLOVE s word 24398, basic model fit 3029, Another difficulty was to find the most optimal hyperparameters 31672, Evaluate Model 32098, How to get the common items between two python numpy arrays 39685, Noise Removal 32045, First let s load the data 20189, Univariate Analysis 34840, Building a Sequential Model 1332, Wrangle data 13700, First we ll drop the Cabin and Cabin Data columns 28187, We re trying to build a classification model but we need a way to know how it s actually performing 12629, Submission 12939, How Big is your family 4825, PoolQC NA means No Pool 6395, Data Type of columns 34666, However in 95 of all cases the monthly sales volume is not greater than 5 40744, how inside the Neural Network work is done 13762, Tune Models 42271, created 13713, Replace missing Age values with mean 7569, Handling missing values 34667, Total sales behaviour depending on month year 10898, Since sex and family status are 2 categorical variables with 2 unique classes they are label encoded 10564, Evaluate Feature Significance 11127, explore these outliers 39345, Multiplying transpose of matrix generated from step 4 with Co variance matrix from step 2 9849, Feature engineering 2558, Loading the H2O package 1644, Explore the target variable properties 27876, Sales Correlation between stores 6784, Age 3696, Missing Values 40664, we have 0 27029, Since we have 200 feats we get 200 pvalue for each sample we can multiply them together 31833, Steps to create a level 12 custom metric that work with any training data even if you are using a subset of product ids 23798, After we dealt with the target let s move to the features 29803, Pretrain Word2Vec 3722, train our models 15940, Cabin 17975, Fare 27604, With the threshold obtained the grayscale image can be converted to binary 39294, RAW FEATURES 16478, Age Distribution 16484, Parent and child on board 7935, Gradient Boosting Regressor 20894, Setup of the gradient descent parameters 42069, Using sklearn OneHot encoder for pre processing 34616, Feed Forward Neural Network 6927, Here I plot missing values 4864, Finding Skewness in SalePrice 6884, And now for the test data 8066, Building remodelling years and age of house 23465, Using polynomial on the dataset 2456, Remove quasi constant features 5940, check the missing values in NULL 20618, Random Forest Classifier 31053, IP Address 13484, LOAD LIBRARIES 22690, LSTM models 21468, Below is the code to create the augmented database where you can change some parameters as you like 32772, Concat train test data 33667, Date font 43212, Generate the submission csv 32263, Relationship between numerical values 5108, Feature Creation 41226, We create a standard function so that we can have similar metrics displayed for different algorithms 33199, The image values are transformed into a float32 array with values between 0 and 1 suitable for neural nets processing by dividing with 255 41294, Build a LSTM Model 14129, Observations 11631, Since the steps of each model would be similar to each other I use function to wrap those processes into 1 line 18846, Having invoked the t SNE algorithm by simply calling TSNE we fit the digit data to the model and reduce its dimensions with fit transform 7575, Skewness and Kurtosis 36357, Examining the pixel value 2639, Most of the passengers have embarked from Cherbourg Southampton 36296, try on scaled data 1830, Submission 26833, Converting to Datetime to Month day hour etc 505, our data is ready to train 21017, Heatmap 34259, Lag features 32416, Preparing submission file 19526, Checking Lineage of RDD 28543, Predict using the trained model on the testing data 5329, Display heatmap of quantitative variables with a numerical variable as dense Similiar to heatmap 10768, to predict 32497, Compiling the Model 23467, Defining a custom scorer function for the models 7938, The best hyperparams for the XGBoosting model are 14737, Model 2 37030, count them 6291, Neighbours 1228, Visually comparing data to sale prices 31620, here we set thresold to a very low value 250000 30883, use the mean absolute error function to calculate the mean absolute error corresponding to the predictions for the validation set 39828, we are now on the last part first make our test data all ready and then we submit our predictions 4998, this is interesting 37571, check the mean y value in each of the binary variable 1199, Embedded methods 9780, Grid Search Score 13294, Neural networks are more complex and more powerful algorithm than standars machine learning it belongs to deep learning models To build a neural network use Keras Keras is a high level API for tensorflow which is a tensor manipulation framework made by google Keras allows you to build neural networks by assembling blocks which are the layers of neural network 20533, Create train and test sets 33100, Support Vector Regressor 37918, Data Preparation of Test Data 8896, Ridge RidgeCV 40933, Test Gens with TTA and Submission 5075, Progress try just all the categoricals instead 19638, Looks like 6 devices with duplicate rows have different values for brand and model 12052, LASSO Regression 34231, That looks much better 17451, SVM 29778, Load Prepare MNIST data 15169, ROC curve 32375, How are the Image Sizes Affecting Targets in Our Data 11, Lasso Regresson 22105, Add an ImageId Column and Save as CSV file 30420, Define cross entropy and accuracy 13114, XGBoost 9319, The dirty fix I adopted before is to simply recode Po to Fa before creating the dummies so that the mismatch is not there This can be justifiable if we look at the counts of each category 32641, Classification 35182, We are using two classifiers Logistic Regression and Random Forest to check if they are able enough to separate train and test data points 41240, SVM 23904, we have the train embeddings This can be used as an input to other machine learning models 42175, To facilitate the entry of data into our neural network we must make a transformation of the tensor from 2 dimensions to a vector of 1 dimension 26531, Model Evaluation 26177, Missing Values 3597, XGBoost 43208, Model creation 21890, visualize how the context vector looks now that we ve applied the attention scores back on it 38134, Feature Engineering 20761, GarageCars column 3895, Histograms 21446, Outlier 5846, Which of the features are more influencing the target variable 12223, For the fans of the numeric metrics 27023, Bonus Best Single Model Function 5328, Display heatmap of multiple time series 9872, Firstly in order to find missing values we need to combine both train and test data 20239, Feature Engineering 26247, Model Evaluation 34301, we can plot a visualization of all the activations within the netowrk 13039, This section of code is for missing value treatment for age 9304, Tree experiment 32106, How to reverse the columns of a 2D array 9067, perform 3 seperate linear regression models for each neighborhood tier 36655, Median filtering which is very similar to averaging changes the central element of the kernel area to the median of the values in the kernel space 10589, XGBoost 20063, Adding column city names where shops are located 6903, To analyse the data based on the family size first create new column 18896, we compare the tuned parameters and not the tuned ones 34397, Null Model Average 43396, One example image 31898, Fine grain hypterparameter tunning 21250, Training Data is split into training and validation data in the ratio of 9 1 7057, Condition of Sale 33637, look at a sample of records 34762, Visualizing Performance of the model 5204, Based on the relations between features I create new features to increase the accuracy of my tree based model 7278, Fare Feature 7042, Physical locations within Ames city limits 26463, Lets visualise one of the training image 26466, Data Augmentation 11927, The first row is about the not survived predictions 489 passengers were correctly classified as not survived and 60 were wrongly classified as not survived 16590, Scaling up data 27750, Character analysis 40408, Bedrooms 38986, Dropping columns with values 8709, RIDGE and tuning with GridSearchCV 5424, MasVnr 39204, Cross Validation 20141, Generating Output 3933, 15 missing data PoolQC Fence MiscFeature Alley LotFrontage a lot 5649, Drop unnecessary columns 14393, that we ve filled in the missing values at least somewhat accurately it is time to map each age group to a numerical value 31638, Dates 14370, there are only 3 passengers who paid more than 280 e 512 as Fare 3732, Check if there are any missing values 37826, Word cloud for Disaster Tweets 23398, loop through the images loading each one as a numpy array applying augmentations to it and feeding it into the model 41773, Show 12 rows of the network 14756, The age distribution for survivors and deceased is actually very similar 12393, Using polynomial on the dataset 36127, Test Trsin Split 9897, calculate the ratio of survived family 4566, Applying XGBOOST Regressor 11247, Encoding categorical variables 3978, Make predictions on the test set 41521, We can interpret from the graph that people who surived had paid more fare 3947, Impute Basement related features 2343, Boosting Family 28304, Making Prediction by training model 5487, Data Modeling 17976, Age 26442, The training score of about 85 already looks very promissing for the titanic dataset 1889, sklearn Models to Test 22441, Correllogram correlation plot 145, Feature Importance 12788, Exporting the predictions and submit them 35489, Training 32219, Delete first three months from matrix 11913, Age 22125, so we have a drop of 0 31699, Outliers 36558, As we have much more cold targets that hot I m not surprised that hot targets occupy only a small part of the data per cluster 14182, Loading our dataset 17384, we have the following 14395, We can drop the Ticket feature since it s unlikely to yield any useful information 40048, And let s pick a resize shape of Bojan Tunguz resized images 538, Chance to survive increases with length of name for all Passenger classes 7417, Check if training data and test data have the same categorical variables 5461, How do we determine what are the important features 2681, Univariate roc auc or mse 11468, Random Forest 15002, Basic Visuals 39725, I think we can drop the name columns now as we won t need it 15843, Fare 27038, Gender wise distribution 29749, The classes are not equaly distributed in the train set being around each with values from for to for also plot a graph for these 39115, LogisticRegression With under sampling or no under sampling 7671, Numerical features 15047, Noble 8505, Numerical Features Bivariate Analysis 2208, XGBOOST 19926, Since outliers can have a dramatic effect on the prediction i choosed to manage them 17443, Cabin 37883, Alpha 32586, An easier method is to read in the csv file since this be a dataframe 35149, Compile 3 times and get statistics 22011, For large datasets with many rows one hot encoding can greatly expand the size of the dataset 25955, Loading Product csv 2478, Grid Search CV 6184, Bivariate Data Analysis 19885, Time Related Features 20614, Modelling 42396, How do stores differ by State 7290, KNN Tuning 12054, Ridge Regression 29107, Visualization 32016, Now we fill NaN rows with the help of Sex column Most frequent categories are Miss and Mr we assign Miss to NaN if female and Mr to NaN if male 42558, Loading the data 13673, The method tells us the data types of the features 43012, Data split 34489, Time to train the model 1069, Blending and submitting the FINAL AVERAGE OF 3 REGRESSORS 18729, The first column is the probability of cat and the second column is the probability of dog 15338, train test split Split arrays or matrices into random train and test subsets 34383, There s a lot of noise in this graph 6227, Target feature SalePrice 4088, Getting hard core 36003, I scale the parameters to stop genetic programming from having to find a good scaling as well as a good prediction 6477, Relationship with numerical variables 13185, Clearly Passengers with less then 1 parch are mo likely to survive latter let s create groups with this information 30668, Text vectorization 14766, Assessing the model s performance based on Cross Validation ROC AUC 28739, X train data 4222, Data Modeling 25685, System When there is NO SOCIAL DISTANCING 10395, LightGBM 19876, Standardisation 1844, Significance of Categorical Features for SalePrice 17039, Before constructing correlation matrix we need to convert categorical features to number 42607, Losses and Optimization 38468, Plot heatmaps with month on x axis and features on y axis for train and test 2496, Observations 28818, Day of the week 22218, Train 13467, Exploration of Passenger Class 28193, Stopwords 16229, As some of the column contains values in string format so first we indexed them using StringIndexer A StringIndexer assign unique integer number to each unique string values 12596, Load modules 8031, There are outliers for this variable hence Median is prefered over mean 12422, No there is one missing data in GarageArea we have to handle by filling with mean value 18258, Model Evaluation 38295, Build and fit the final model with the best parameters 18223, Training 21244, The Generator Model 38736, that we have extracted titles from names we can group data by title and impute missing age values using the median age of each category 7597, Boxplots for categorical features 24292, take a look to the first row 7150, TRANSFORMING DATA 34328, Add data augmentation 15084, Random Forest 17860, check the features importance for the 5 out of 6 models 3507, Parameters of the best model 40644, Plotting Word clouds 16403, Since median can only be computed on Numerical Values so we need to drop the Name for numerical data 24148, Product and Hour of Day Distribution 43214, Since there are many title columns now which do not exist in train and test dataset add those 32325, variable types 13895, Seems that Random Forest and AdaBoost perform better 14443, Update Age with ordinal values from the AgeBand table 16118, k Nearest Neighbors 32855, How sales behaves along the year 2031, Looks like the distribution of ages is slightly skewed right 24888, Time to get prepare data for our model 6836, One Hot Encoding Nominal Features 9506, Import or Load all of your data from the data folder 3886, Continuous and Discrete Attributes 23639, Observations 37867, Distribution of numerical features 30771, Once instantiated the ensemble behave like any other Scikit learn estimator 37819, Define function to remove patterns 25012, Time Features 466, Merging numeric and dummy features 8294, Bagging oob score False 26501, Pooling is plain max pooling over 2x2 blocks 3517, examine categorical features in the train dataset 9354, New title featue 23248, We re deleting the Cabin column because there are too many minus values 22893, Lets Visualize with the class imbalance thing 29753, Prepare the model 12007, checking R squared 30178, Sigmoid function 15285, Creating New Categories 35547, Models include 5284, First of all we are going to train a baseline LR model 24984, Dropping categorical variables with too many unique classes 31677, We are going to artifically add noise to our data 14719, TEST DATA PRE PROCESSING 39966, Statistics 11711, Convert to Categorical 18505, Test set 27923, Optimal Tree Structure 24295, let us plot the first 10 images 11981, Ordinal categories features Mapping from 0 to N 6298, Model feature reduction 35425, Defining the CNN model 9387, BsmtFinSF2 Type 2 finished square feet 42394, Do total sales correlate with the number of items in a department 26193, Converting DICOM in NIFTI 22013, Use the next code cell to one hot encode the data in X train and X valid 9952, Scatter Plots 34381, Relative Time Scale 17007, Amount of missing data in both columns is insignificant 38971, so we pad for length of 64 29683, Reshape images 7474, In this section I plot some of the parameters that have an influence on the outcome 24925, note that one does not have to use only words 14337, Applying the ML Model 5987, Data Test 13431, Encoding and Categorizing Data 15666, Hyper Tuning the Models 3845, option2 replace with median age of gender 19521, Fetch Ordered Elements 3755, the dataset is ready we are now ready to explore it 20760, BsmtHalfBath column 16230, data is converted which are required to predict survival into vector form by using VectorAssembler as VectorAssembler is a transformer that combines a given list of columns into a single vector column It is useful for combining raw features and features generated by different feature transformers into a single feature vector in order to train ML models Normalizer is a Transformer which transforms a dataset of Vector rows normalizing each Vector to have unit norm It takes parameter p which specifies the p norm used for normalization This normalization can help standardize your input data and improve the behavior of learning algorithms then we normalize our data by using Normalizer 18058, Word Cloud for Negative tweets 23054, now lets do the fitting process again i 26496, In this case there are ten different digits labels classes 25756, The original color is still visible as the color of the rim 1015, Nice now let s apply this key technique ourselves We use the basic version of k Fold with 5 folds from our friend Scikit learn 7593, Another option to highlight the correlation of SalePrice to all SF and 13902, Data Encoding 5829, Checking for null values in all feature of this df 40380, Here I created 5 folds and appended the 10 dataframes into a list 24415, Area of Home and Number of Rooms 14206, we pick the one with best accuracy 471, Numerical Features 32513, Model 3 Using less layers of VGG19 6993, Year Sold 5973, Time to train every model with further hyperparameter tunning extension 40004, Image names 19622, PREDICTING RESULTS 43213, Organize and modify features 34954, The confidence interval of the model coefficient can be extracted as follow 12899, get the dummy variables for our non ordinal categorical features 12297, Title 14431, go to top of section engr 38037, The feature importance we get from random forest is very similar to the list we got from decision trees 17425, so how many people we got here 11270, Of these SibSp Parch and Fare look to be standard numeric columns with no missing values 30768, There you have it a comparison of tuned models in one grid search 12327, Can not I delete it if it is not in Test Because the deletion is less risky but 38220, It also contains 2323 duplicates that we should remove 9771, These are the remaining categorical features as dummy variables in our dataset 7953, Tuning on weight initialization 3577, Through such a process I decided on outliers below 10250, Go to Contents Menu 39737, Both stories tell a similar story that smaller families tended to survive more than larger families 32061, convert these images into a numpy array format so that we can perform mathematical operations and also plot the images and flatten the images 3145, Bvariate or Multivariate Data Vizualization 18382, Set the variable q to any disaster related keyword or anything else of your interest 7895, the AgexClass can be dropped or not as I experiment to increase the general performance of the model in the next steps 31605, Cleaning the data set 26081, Adding new layer and Dropout to avoid overfitting 18917, Fare Feature 33602, Load and Visualise the data 36337, Train the model 26646, Similar analysis to dfDocTopic we have 11759, The LGB Regressor Model also was overfitting a lot so we change some of the paramters to help with that 14349, pandas profiling 40929, Image Modeling 16359, Exploring the Pclass vs Survival 11024, there is only one null value in Fare attribute and we are going to fill it with median value value 14669, Logistic Regression 33052, Predict test data 24955, Fare in Test set 3559, I was reading about how to select good feature from so I decided to try it now that I can t add features on myself so let s do it 28007, Processing Data with The Model Fitting 12839, Gaussian Naive Bayes 37223, Much better Lets deal with the contractions now 43277, Instancia uma nova RandomForest 35195, Well both of these feature transformation trials have proved to take symmetrical bell shaped curve now 30092, About Coarse dropout technique 1042, One hot encoding 15359, SHAP Dependence Contribution Plots 10642, first deal with Null values in code Age code 42024, Extracting the first word 39005, Implement cost function with SIGMOID activation for the last layer 18644, There are few peaks and troughs in the plot but there are no visible gaps or anything as such which is alarming 18664, Prepare data for processing by RNN and Ridge 2375, Shuffle when using cross val score 10688, Ridge regression is an L2 penalized model where we simply add the squared sum of the weights to our least squares cost function 35857, For the 1st train I ll boost the training with a 0 26768, My models 1710, Imputing using ffill 30314, Convert a few float type samples in text and selected text columns into strings 20250, Final look 40024, As reading the dicom image file is really slow let s use the jpeg files 41128, Multiplicative 10195, Calculating best metaparameters 31309, We have a total of 1115 Stores all across 14714, KNN Parameter Tuning 656, Splitting the train sample into two sub samples training and testing 36992, Fruits like banana strawberries 6376, Find out the mode values 6748, Detect outliers using IQR 6370, We stack all the previous models including the votingregressor with XGBoost as the meta regressor 7485, As fare is a continuous parameters we benefit from grouping it 12627, Model training and prediction 30349, data looks a bit dirty we might get an overly optimistic prediction because the last number is not the final one for instance 24458, Thresholding is a very popular segmentation technique used for separating an object considered as a foreground from its background 39209, Analyze not real tweets 15976, We can appreciate that a more expensive ticket fare increases the survival rate of the passenger that buy it 34921, Some additional features 34524, Interesting Values 27941, Show predictions and feature importance 35808, Detect same shops 12024, check how correlated are the predictions on test data of all the models applied in this kernel 22910, F1 SCORE 32381, Getting Landscape Attributes from Images 1379, Interesting 28413, Label Encoding 10542, Merging for XGB Regressor 28961, We compare the difference between All years feature with SalePrice 7105, Logistic Regression 1670, introduce the new look of Sex and Embarked 10877, Making a check point 26215, And if you are interested in just visualizing one certain image s bounding box plot you can first extract the chosen image s dataframe and convert the bounding box of the image into a 2d array apply the draw rect function to plot 7033, Pool quality 35790, LightGBM 7609, Pipelines with default model parameters 33699, Monthly Count font 32062, now create a dataframe containing the pixel values of every individual pixel present in each image and also their corresponding labels 17735, Removing variables with too little coverage 7741, create model 38773, XCeption 2427, Ridge Model 24583, Initialize and Reshape the Networks 18477, Promo2SinceWeek Promo2SinceYear and PromoInterval 24370, Floor 40196, Data Agumentation 25633, MODELING 37472, Tokens 41971, When using Google Colab 5894, Train data of these columns used for label encoding 13924, Merge train and test facilitates the exploratory analysis and the feature engineering 30474, ColumnTransformer use cases of passthrough and drop 20265, Convolutional Neural Network CNN 4519, Support Vector Regressor 2117, Feature selection 41951, The dataset contains 10 000 tweets that are hand classified 29162, MasVnrType Fill with None 14200, we gost a much cleaner dataset 17732, XGBoost Parameter Tuning RandomizedSearchCV 9424, Histogram 40271, Basement Quality vs Sale Price 12217, Other evaluation approaches 12750, Some titles only have a handful of occurences so we can replace them as Other 5377, Cleaning Data 25001, To prevent overfitting 42389, Creating Tidy Dataframes Capable of being fed into Models 22222, Veri setinden g rselle tirmeler Vizualizations in dataset 26876, This is the simplest approach 24671, DATA CLEANING 17991, The title of the passengers can be extracted from their names 27405, Make predictions on the test set 32974, Fare is string with number at the end Two consecutive ticket number means they are bougth from same place or they got same deck on the ship 34700, Linear extrapolation 18911, Name Feature 4619, We notice that this is a small dataset 6101, Submit 13773, Ticket Cabin have different levels compared between train and test data 8561, Renaming Columns 15704, Age vs Survived 38179, Plot a Model 12071, Predictors Target Split 33487, Features Select features 19740, Model Formation 29620, Imputing filling missing value by its most frequent For example Embarked attribute consists of 3 differrent port C Cherbourg Q Queenstown S Southampton 21502, Plot histograms for image sizes for the analysis 9984, outliers 9258, The Undisputed XGBoost with RSCV 25830, Loading Data 9042, This narrows down the features significantly 16401, Sex Male Female as 1 0 26526, Has to do with Trump 23939, TF IDF 22457, Joyplot 42037, Groupby mean cmap 20757, SaleType column 9055, This relationship isn t super clear 1608, How is this feature different than Family Size Many passengers travelled along with groups Those groups consist of friends nannies maids and etc They weren t counted as family but they used the same ticket 4941, Outliers 20187, Clean the data 40186, Subtext analysis 5227, FA 15023, Survived ratio of female is much higher than male 27008, Callbacks 10540, Labeling only for XGB Regressor 10869, Using PCA to reduce dimension 30596, The dataframes now have the same columns 9404, Standard Error 24525, Age distribution of the customers 28862, Plot Example 37287, These are the weights that were found offline using hillclimbing 15163, One Hot Encoding 4992, Learning 24894, Logistic Regression 464, Fill NA s in categorical data 24112, lets predict for our test data 27211, Before going any further with training let s take a look at sample photos from both classes 28670, Alley 16613, Handling Outliers 5321, Data 31947, Submission 5720, Getting the new train and test sets 14835, Prediction and Submission 35175, Experiment Replacement of max pooling by convolutions with strides 19694, Convolutional Neural Network CNN 3293, we create an object of this class and use it to fit and transform the test data 617, Admittedly these are quite a few grouping levels but 30 vs 20 are numbers that are still large enough to be useful in this context 2503, Correlation Between The Features 14419, go to top of section prep 35767, maybe do a groupby to make this table more manageable and easier to read 10995, Lets explore the dataset that we imported freshly br 5741, Loading packages 2296, Pandas take a peek at the first few rows note what may be categorical what may be numeric 14800, Lets try hyperparameter tuning for GBM 23638, Paragram Embeddings 926, Optimize Lasso