3448, we make some dummy variables for Title and add them to the dataframe 28220, making feature matrices for modelling 29402, CHECK FLOOR AND MAX FLOOR 4319, Ticket holders with a zero fare 16242, compare our models through visualization 25823, Truncated SVD on continuous variables 28605, YearRemodAdd 1387, Evaluating the model 15914, Surname 22081, Prepare our Data 37507, add Learning Rate annealing 8335, This is normal distribution As a custom let s have a look at correlation matrix 4425, Way better check the skewness of data 19902, Top 10 Sales by Shop and item Combination 10259, Converting categorical values to numeric values 38514, Ngram exploration 19923, Mass Histograms 8826, Feature Engineering Cabin Deck 6914, PIPELINE 15145, One Hot Encoding 34682, Looking at the test set 8871, Removing Irrelevant or High Correlated Columns 13551, Crossing Embarked by PClass and Survived 24834, use KNN 7410, Neighborhood Besides the zoning classfication neighborhood also makes a difference Houses located at Northridge Heights NridgHt have higher sale prices than those in other areas generally but the variance is large The difference between median price of MeadowV neighborhood with the lowest house prices and that of NridgHt is over 37029, let s analyze the price 38086, For one hot encoding we use the onehotencoder from sklearn preprocessing library 20237, Cabin 8934, Total Square Footage 4802, We are dropping the Id feature since it would not add any useful information to the model 31045, Duplicate Sentence 11483, BsmtFinType2 BsmtExposure BsmtFinType1 BsmtCond BsmtQual BsmtFullBath BsmtHalfBath TotalBsmtSF BsmtFinSF1 BsmtFinSF2 BsmtUnfSF 29334, with the PCA variables 20723, BldgType column 14779, SibSp 25020, Beautiful 32685, Now we have two folders containing each train and test images 36056, Count Monthly Mean 42821, Matcher and Bipartite Matching Loss 21404, it s time to separate into train and test database 31661, Observation 34092, The most interesting question regarding manager id is how to derive a manager skill feature 15873, Number of estimators and max depth 1909, Univariate Analysis 1938, Kitchen Quality 42573, Kaggle returns the score rounded to 5 digits meaning that the contribution of a single value to the log loss lies in the range of 26850, Words Counts Insights 16988, Gradient Boosting classifier 16393, Practicing Random Stuff 876, Data wrangling 18134, StackingCVRegressor extends the standard stacking algorithm implemented as StackingRegressor using out of fold predictions to prepare the input data for the level 2 regressor 37459, Remove Punctuation 10755, We can sort these people by Fare in descending order 28822, Misc 6813, Ensembling is the science of combining classifiers to improve the accuracy of a models Moreover it diminushes the variance of our model making it more reliable You can start learning about ensembling here is better than one ensembling models 611ee4fa9bd8 7634, Correlation of predictions 37041, Certain parameters were chosen for the Kaggle kernel 5566, claen our data from outliers 14915, Features Generation 41417, Feature scaling 36390, Example the 3 E and G values for the cat92 be replaced by NaN during encoding 2322, Fitting a Logistic Regression 8825, from both figures I can assume that if a passenger have family onboard the survival rate increase to approximately 50 19866, There we go with remaining dataset after eliminating the outliers 37160, SUBMISSION 32949, Almost zero 12284, Pie Chart 38765, The train accuracy is 83 3712, Handle Missing Values 36920, Observations 14470, and conversion of object data type to categorical is necessary for reducing the space in memory and decrease time in computation 24166, Augmentation 29322, Model 3 20440, bureau 24466, Fake Images 26929, let s train the models 21854, If we make a recap FNNs from my previous notebook had an accuracy of 80 CNNs had and accuracy of almost 90 while RNN reached 97 Lastly LSTMs were the best performing ones 99 accuracy 3340, now check if any pending nan left in age feature 37213, Choose Embedding 9810, Heatmap 16439, Age 28878, We wil use a sliding window of 90 days 20828, If you are assuming that all records are complete and match on the field you desire an inner join do the same thing as an outer join However in the event you are wrong or a mistake is made an outer join followed by a null check catch it Comparing before after of rows for inner join is equivalent but requires keeping track of before after row s Outer join is easier 1350, Quick completing and converting a numeric feature 3429, Embarked is a categorical variable that can take one of three values S Q or C 24035, Visualizing the data that be used for training 7267, Missing Value 13532, For optimal 3 features 24519, Drop all the other missing values 22658, Loss Function 9040, Information Gain of Categorical Norminal Variables 32864, How much each item s price changed from its lowest highest historical price 27456, Some question are written only in lowercase 41992, Locating loc To read the item in a certain row and certain column 23630, Making predictions 20630, Stopwords Punctuations present in real vs fake tweets 31404, The train csv file contains Label column with pixel values If we think it as filename label pair all we need is a filename for each of our data 37674, Checking out how the data looks 2100, Here it looks it makes a difference only in having a regular vs irregular lot shape 16995, Tuning model 21774, Convert the products feature columns into integer values 25887, Histogram plots of number of punctuations in train and test sets 20497, External Image Names 26176, We first need a few things imported 28502, Creating Features 24427, Preprocessing the data 29981, Fit the model 34105, Age group distribution of covid 19 affected population 8159, we ll map the correlation of independent variables so called collinearity 41199, we can feed this data to our model 10347, Label Encoding 18726, use a list comprehension to extract the ids 32382, Getting Data Ready For ML Algorithms 35520, In this part numerical features have been analyzed 27208, In more than half of the patients in our dataset the cancer is found on the torso 38824, Define PyTorch dataset 29928, Plots of Hyperparameters vs Score 35750, alternate data source using top n features from Sequential Feature Selector 6762, Checking Skewness for feature MiscVal 20906, Create CNN model 27890, The event window is located on the x axis where zero stands for the given event day 1605, Fare 4254, Nulls in training set 3691, Submission File 41786, Early stopping terminates the training of the neural network model at an epoch before it becomes over fit We set the patience to epochs 5 41259, Create A New Model 25862, Classification Report of tune sgd model 9883, Correlation Between Embarked Sex Pclass Survived 14903, Pclass Age Sex vs Survived 35634, Nearest Neighbors for our eight image 23846, Taking a total of 378 feature count to 185 38043, Checking for Skewness of the data 27499, Shallow CNN 2 with data augumentation and regularization 14434, Plot Overall Survival 473, Categorical Features 40445, LotArea 23616, Check if there are any missing values 8307, Using Single Classifier 32322, I need only the following three features from the dataframe 1616, Random Forest 39312, Save model and test set 2944, Feature Correlation 33088, Time to rebuilt the train and test set now 8533, Target Variable 27828, Extract xtrain ytrain 22752, Plot Infectious Population and Total Infected Population for Multiple R0 13855, Creating new feature extracting from existing 27331, Shape of files 17949, Encode Name 5038, The upper bound is 466 075 USD let s filter out samples beyond that cut off 23087, 82 of accuracy during a cross validation is a correct score for the first shot in a binary classification 10673, Final Adjustments 24047, Target log transformation 32300, Displays location of a country 42882, Tree plot 17338, XGB 305, XGBoost 33295, Pclass sorter 21117, Scoring 27541, Display the distribution of a multiple continous variable 15408, Indeed there is quite a big difference between the average age of married and unmarried women in all passenger classes 10079, Bivariate Analysis 4562, Checking Skewness 22532, Parch vs Survived 35932, Binning Age 21516, Splitting The Data into train and validation set 32224, This is just a sample of what the different numbers that we re trying to classify look like 36978, Submission Files 30318, For test data set default start end positions to dummy integer 35689, CatBoost Hyperparameter Tuning 37346, Add the remaining layers 3174, create a test function just to comapre the performance 20172, To find optimal combination of parameters to achieve maximum accuracy using GridSearchCV from sklearn library GridSearchCV learn org stable modules generated sklearn model selection GridSearchChtml does exhaustive search over specified parameter values for an estimator 15295, Logistic Regression Model 37231, Lets convert heights to a longer format 13842, Correlating categorical features 31907, Makes predictions 38745, Based on these visualizations we can conclude the following 1936, Bathrooms in house 13756, Obvious that females had a much higher chance of survival as compared to males 22333, Stemming 33581, Inference 8881, We can create another feature where we can monitor the age of house from its selling date to the last time it was remodelled 10309, Quick and Dirty Look at Validation Set 12663, Our test set does not specify which passenger survived and which passenger did not 31433, Checking Being Loss 1095, Categorical variables need to be transformed to numeric variables 16933, Data treatment Feature engineering 26990, Submit 4122, Data Preprocssing and Machine Learning 29789, Sample few noisy and original images 36347, Implement a Neural Network Multiclass Classification 13542, Summary of df train 34020, Heatmap 34512, First we can establish an arbitrary date and then convert the time offset in months into a Pandas timedelta object 1254, Fix skewed features 24349, As we know SWISH activation function recently published by a team at Google 26254, Evaluation Functions 16716, Decision Tree Classifiers 16904, Create a dataframe with Name Fare Pcalss FamilySize 12033, Correlation 4110, It is positively skewed 6788, Fare 32745, From my kernel 8075, For the rest we just use a loop to impute None value 13622, Categorical variables are ones which are not numerical 21589, Filter in pandas only the largest categories 3017, After imputing features with missing values is there any remaining missing values 12603, find correlation between Numeric Variable 4993, Predicting 939, Clean Data 34064, SibSp Survived 25941, XGBoost 2330, Sklearn metrics good ones to know 38211, Resize the flattened images into 28x28x1 pixels images and regularize it by dividing it with highest value ie 255 3731, Drop not so important features 36351, Define the Network Parameters 33703, Color based on Value font 3578, Data Cleaning Missing values 4451, Train the selected model 41471, Determine the Age typical for each passenger class by Sex Val 16256, Misc 36192, Seniority requires DT fecha dato as POSIXct strptime x DT fecha dato format Y m d conversion Sometimes it works sometimes it works more then Kaggle kernel allows 40449, LotConfig 18165, Getting common words from question1 and question2 in dataset 24325, use robustscaler since maybe there are other outliers 27957, For large datasets with many rows one hot encoding can greatly expand the size of the dataset 7726, TotalBsmtSF Total Basement Square Feet 18953, Display distribution of a continous variable for two or more groups 28595, GrLivArea 15849, Family Size 5916, SVR 28410, Generate Predicitons 23888, Finished SquareFeet 12 20454, Application data 20621, Survival Prediction on Test Data 15821, We have deleted all the nan values 25294, using this let us demean the contour data 4723, To starts with I import necessary libraries and loaded the data set with pandas read csv method 41870, Parameters to be investigated 3276, Updating FireplaceQu LotFrontage MasVnrType and MasVnrArea PoolQC MiscFeature Alley Fence Electrical Functional SaleType Exterior1st KitchenQual Exterior2nd 19578, Items Analysis 18336, Moving forward i am going to check standard correlation coefficient between every pair of attributes using the corr method and try to decipher some relationships between variables 26636, We create two indices correct and incorrect for the images in the validation set with class predicted correctly and incorrectly respectively 31854, Trend Features 32150, How to find all the local maxima or peaks in a 1d array 21578, Named aggregations avoids multiindex 4007, We write our own implementation of the algorithm 38668, Decision Tree 32639, URL 28189, Accuracy refers to the percentage of the total predictions our model makes that are completely correct 22826, Shops Analysis 28139, Predicting the given test dataset 38779, Select investment sales from test set predictions 23567, A fresh beginning 30988, The following code repeats this plot for all the of the numeric hyperparameters 25456, Building the top model 13279, We can use Logistic Regression to validate our assumptions and decisions for feature creating and completing goals 1923, Garages 32410, Determining number of iteration 16031, Embarked 1900, DecisionTree Model 7452, Examining Missing Values 3666, Checking that we got rid of all the NaN s 12115, Machine Learning 102, Correlation Matrix and Heatmap 15652, Linear Discriminant Analysis 15927, SibSp 24153, we ll get the names of each product in the morning and afternoon groups in order to recreate the product list in the original chart 13675, Exploring the data 26282, As a model this time we use GradientBoostingRegressor Let s train it and check the Mean Absolute Error 26951, Model Evaluation 36380, Training Testing 30367, Test PIL Image 897, for Random Forest classifier 14560, Highest number of Siblings Spouses were 8 in number boarded from Southampton font 8668, Trying to use embeddings for encoding categorical features 27462, Combos 17871, Classes of some categorical variables 25847, Cleaning Text 4886, You need to do the same changes in test dataset aslo lest merge test and train 30682, Submisson 54, Gradient Boosting 12103, Making Training Validation Test Dataset 6928, Four features have very few values drop them for the first analysis 38670, Gaussian Naive Bayes 16263, SibSp ParCh 38501, Analysis of the Sentiment Column 9474, Setup This function initializes the environment in pycaret and creates the transformation pipeline to prepare the data for modeling and deployment setup must called before executing any other function in pycaret It takes two mandatory parameters dataframe array like sparse matrix and name of the target column All other parameters are optional 6380, Find out standard deviation 13477, Assessing the model s performance based on Cross Validation ROC AUC 38658, Age Range 18962, Display distribution of a continuous variable for multiple categories with hist curve instead of bar 20751, SsnPorch column 29883, Visualization of model outputs for all training data 38968, instead of using all the 400000 word vectors lets use only vectors form words present in the train and test data codes mean that we are giving each unique word a index number and storing in word2idx dictionary and also creating a new embedding dictionary which maps those numbers to a coeff from glove embeddings If the word does not exist in the glove embedding then we give them a random coeffs of same dimension 35128, Working With TIME 21844, Understanding the Model 1048, We gather all the outliers index positions and drop them from the target dataset 4940, let s check if there are any missing values left 21655, Check the equality of 2 series 27216, Submission 40714, Visualize Digits dataset 8094, Pclass vs Survival 42919, Quantitative 25938, Correlation 24739, DBSCAN Density Based Spatial Clustering of Applications with Noise 20589, Random Forest Classifier 34222, We ll make a get items that simply returns our good images 17610, Ensemble votring 9612, Histogram 32143, How to find the maximum value in each row of a numpy array 2d 27762, Lemmatizing and stemming the text 18162, Fields in both input files are 33691, Calendar 12216, And the best parameters are 11101, Drop features where more than 20 records are null 6141, Features engeneering 26944, AMT INCOME TOTAL 15032, Distribution of Survived passengers on Embarked in Train Set 42565, it gets interesting 26865, The gaussian blur works the sme way except it uses a ponderation in the computation of the local mean around a pixel to give more weights to closer pixels 37500, numeric values related to SalePrice 28600, The different categories exhibit a range of average SalePrice s 30382, Avoid leakage take only non overlapping values for training 33813, Improved Model Random Forest 35346, In this case the average values do not vary a lot 37951, The Confirmed Cases are on the left Y axis and the Fatalities on the right Y axis 29718, Whole pipeline with estimator included 21573, Creating a time series dataset for testing 8528, Basement Features 10977, Top influencers 12953, Basic data analysis 33676, Least Last font 11015, Lets map sex first 37655, Save the model and model weights These files going to output folder as expected You can download them 7710, Skewness 35530, In this part a blended model was created with regression models 20256, Even if when I use pytorch for neural networks I feel better if I use numpy 12371, Applying the replacements 8078, Features Simplication 294, Pclass 3596, ElasticNetCV 18948, Relationship between variables with respective to time with range slider 37138, Softmax Activation Function 32570, Objective Function 34044, we proceed to parsing using the function pandas to datetime 26476, For Submission 33287, Age Filler 11659, Naive Bayes 13233, Train data is approximately twice as large as test data 20060, Prepare the submission 34271, Predict 12918, Missing values 9937, am going to replace the missing value in the Fare column by the average fare of according to the Sex p 24672, DATA PIPELINE 18297, preview our function 1171, impute all incongruencies with the most likely value 42059, Using python and math to display max min mean 28489, WOW reduced from 35MB to 24MB 15648, Extra Trees 17255, Load Data 6499, A lot of difference between the selected features 8017, it s Catergorical data 1161, use test data 6011, Seperti yang sudah saya jelaskan di bagian intro saya akan menggunakan algoritma Random Forest Regressor lalu pakai Randomized Search sebagai Hyper Parameter 17942, Preprocessing 24752, Box Cox Transformation of skewed features 34661, Replotting in log10 scale 245, Model and Accuracy 1884, There are 3 values for Embarked S C and Q 22680, Load embeddings 40377, Defining the paths 30884, Improve the model 16701, Create new feature combining existing features 13454, 20 of entries for passenger age are missing 1674, Great now let s have a look at our Survival predictions 32845, Training 28087, Run on training data 6588, Same can be done as follows 24991, As a sanity check checking that the number of features in train X numerical match the total number of numerical features 13277, Creation of training and validation sets 32137, How to convert an array of arrays into a flat 1d array 32869, Train validation split 8293, Decision Tree 2257, Embarked Pclass and Sex 11689, The accuracy of logistic regression model as reported by Kaggle is 77 10160, Stackoverflow developing survey 31643, SCORE 5456, work through a single point sample calculate a 95 Confidence interval 37118, category name 14277, Bagged Desition Tree 15540, Try Other Models SVM and Random Forest 30180, LSTM Model 2935, Data Processing 164, This is good enough but there is a neater way 8479, at this point we not cut any additional outlier but we not make use of the sales price transformation in your log1p and thus avoid the linear pattern of the residuals 16498, KNN 23023, Sell Price Analysis 37017, Top 20 categories by average price 8782, One hot encoding for title 13711, COMPLETENESS ISSUES 36875, features reshaping 1d vector to 2d images 43247, usually variance of is sufficient to explain the variation in data so we first train data by taking the top n principal components which can explaine the variance of 10276, Random forest 15842, SibSp and Parch 38704, After encoding 7307, Observation 29419, we have to stem our text be using SnowballStemmer as it is quite good for the job let s just get to the code 39946, Ridge regression 11048, Over the course of data preprocessing many functions are used that help in extracting features p 42403, Sarima Predictions 20755, MiscFeature column 5806, Solving the problem using XGBRegressor 30941, Visualizing Interest Level Vs Bedrooms 15629, Missing Data 4561, Adding one more important feature 11973, We fill BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2 GarageType GarageFinish GarageQual FireplaceQu GarageCond with None Take a look in the data description 21588, Combine the small categories into a single category named Others using where 20830, we ll extract features CompetitionOpenSince and CompetitionDaysOpen 31685, use the autoencoder to produce denoised images from noisy ones present in x val 18310, Mean Encoding for item id 149, XGBClassifier 12036, A few categorical variables are ordinal variables so let s fix them 9428, swarmplot 2675, MI for Regression 21198, L Model Backward 15611, Name Length 22927, Just one last thing 10036, Extract features from Name 14795, SGD Classifier 5430, Fireplace 5468, From the RF object we can pull feature importance and plot 11046, let us first import out libraries 24310, take a loot to the first prediction 18324, Update parameters 32515, Pre processing the features 34013, No outliers 31686, Seperate models for noise reduction and classification are not very practical hence we are going to combine them into single unit using the Model class 12512, All set Moving on to incorporate this data 665, Decision Tree 9745, Parch 2936, Check the distribusion of Prices 16463, Handling categorical variables 24061, Finished trialwith value with parameters 11951, Our first goal is to create the models and find their best hyperparameters by running the model individually by gridsearch cross validation 3220, 2D Histogram 8139, the features with a lot of missing values have been taken care of move on to the features with fewer missing values 14844, Since some of them are first or second class passengers I decided to remove zero Fares that might confuse my model 15350, Validation 27836, CNN 15876, Or just the best parameters 9007, Deal with Null Values that contain information content 15874, We have set out a total of 4 times 4 16 models over which to search 4891, Wohh that s lot s of title 40151, Interestingly enough there are opened store with no sales on working days 32507, Compiling the Model 2 23815, take the variables with high correlation values and then do some analysis on them 11313, Correlation Matrix 32980, Encoding categorical features 1282, Fare 3 2 6 34906, Check some Null s 23425, we analyze tweets with class 1 1862, Random Forest 956, Output of the First level Predictions 27098, The architecture of VGG16 is kept mostly the same except the Dense layers are removed 20523, Looking for Correlations 2451, Regression 37783, Install LOFO and get the feature importances 31712, Here s my function for splitting up hdf5 model files 32768, Label encoding 25420, Uniform day of week distribution 36672, Each vector have as many dimensions as there are unique words in the SMS corpus We first use SciKit Learn s CountVectorizer This model convert a collection of text documents to a matrix of token counts 21071, Function for preprocessing 19045, Load in the csv file 34418, we analyze tweets with class 1 2793, To analyze the performance of models is to use the evaluate model function which displays a user interface for all of the available plots for a given model It internally uses the plot model function 35608, True label is 9 but predicted is 8 15954, Reading the Dataset 8514, Feature Engineering Creating New Features 27209, A nevus is basically a visible circumscribed chronic lesion of the skin 32498, Train the Model 627, Based on this plot we define a new feature called Bad ticket under which we collect all the ticket numbers that start with digits which suggest less than 25 survival 37195, Performing Label Encoding 1753, Median imputation 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 30397, Fitting 4293, Based only on the total square footage we get a R squared of 0 2918, Split into features and class 36998, Hours of Order in a Day 28133, Splitting training and test set from our training dataset to train our model 40269, We want to over fit a simple model on the dataset 41058, Plotting the a few groups with at least two data points to them I can t really tell if group 1 was created from clustering the characteristics 2136, While the model is not performing very well it is also very easy to tune 24813, Low correlation 20795, Filling Numerical Missing Values 14339, Lets to select only some of the features 36855, Normalization 36290, Logistic Regression 20525, Log Transformation of the target varibale 9124, Set up Categorical Ordinal Variables 17362, Kfold for cross validation 5146, Missing values 27337, Reshaping data as images 42985, Creating Word Cloud of Duplicates and Non Duplicates Question pairs 37707, what s next 6139, Checking the full dataset 5135, Discrete Variables 10959, Normality and skewness 3463, we make some dummy variables for the Deck variable and add them to the dataset 6113, By the way let s fill all the missing years with the date of the houses were built 4715, let s fill in the missing values of the age column 24696, let s define a trainer and add some practical handlers 29463, Number of distinct questions 23251, We keep Passenger Id separate and use it for Submission 1534, Ticket Feature 7430, Compared to grid search randomized search is less time consuming so we start from a wider range of parameters with randomized search 2215, Confusion Matrices for 4 models 14424, Use function cabin fillN to assign Cabin letter based on mean Fare per Cabin 31580, Clusters of binary properties exhibit some kind of correlation 3725, Train Xgboost Regressor 16676, Analyze about fare 35252, Difference variable would be difference between length of selected text and length of whole text 18157, Split the dataset raw features 34232, make our true get bbox and get lbl 2875, On submitting this file on Kaggle we are getting a rmse score of 0 1074, SalePrice is the variable we need to predict let s do some analysis on this variable first 31913, Reshaping Data 11757, The random search for the XGBoost took a long time so I put it in here and changed some things 4147, Mean Median Mode Imputation on Titanic dataset 20448, bureau balance 34273, KNN performance on longitude and latitude data 5852, CORRELATION 20719, LotConfig column 8024, Cabin 28190, Natural Language Processing using NLTK 4701, Modeling 12396, Plotting the residual plot for the model 9807, Checking Missing value is present or not in our dataset 8434, Pool Quality Fill Nulls 30824, Mean Average Precision 19882, Power Transformer Scaler 41535, But what of 3d data 4038, Categorical columns decorations 19324, Data Normalization 16405, On this Contingency Matrix we can do some statistical tests like Chi Square Test 9905, Simple Logistic Regression 3861, Splitting our train data into 70 train dataset and 30 test dataset 26297, Training 5353, Diplay surface relationshiop between multiple values 18575, Most number of passengers were embarked in Southampton 8370, CLASSIFICATION 26412, Analyzing the ditributions for different Pclass reveals that for instance some 3rd class tickets are much more expensive than the average 1st class ticket 24400, Final output the predictions to a competition file format 15232, Here we are deleteting Survived Column cause it is target value to be predicted 31008, Model Design and Achitecture 38101, We need to understand that our data set contains 60 000 traning images and 10000 testing images 552, RandomizedSearchCV and GridSearchCV apply k fold cross validation on a chosen set of parameters 8368, Convert Pclass into categorical variable 15076, Fare Group 15595, Missing Data 9871, I am going to concatenate train and test values in order to find missing values 11784, Cabin 40001, Prepare to start 39024, Split a part of the train dataset for test the algorithms 15271, Decision Tree Algorithm 20157, Extracting label from data 34287, Prediction 9423, Calander Plot 42628, Line plot with Date and ConfirmedCases 31116, Correlation between target and log price 14619, It s your turn 23040, Relationship of Lag Variables 11032, Perceptron 27064, Count Locations 43246, define normalize function for normalizing the data PrincipalComponents function to return top n principal components 22124, Blending 23513, There are 4 elements in the class 31681, Proceeding to train the classifier 15730, Evaluate the Random Search for Hyperparameter Tuning 15328, Lets create a new column of fam using SibSp which means number of Siblings or Spouse and Parch which means number of Parents or Children later we be dropping SibSp and Parch from our data set since these values are alreday being used in Fam 35073, Making predictions using Solution 5 18661, Fit Model 41928, Interestingly we have 12 features which only have a single value in them these are pretty useless for supervised algorithms and should probably be dropped 36801, And lastly we actually parse the example sentence and display its parse tree 4960, Final Training and Prediction 27377, RMSE 1 38936, By mean of both Random forest and Xgboos 42616, we ll specifiy how the model is optimized by choosing the optimization algorithm and the cost or loss function The Adam optimization algorithm works well across a wide range of neural network architectures Adam essentially combined two other successful algorithms gradient descent with momentum and RMSProp For the loss function softmax cross entropy with logits is a good choice for multi class classification 22477, Area chart Unstacked 34703, Shop active months 10861, Getting the scatterplot for the top correlated features 21611, Calculate the difference between each row and the previous diff 30000, Seperation of Features and Labels as well as reshapig for CNN input 37176, Create the predictions 40058, Running models with StratifiedKFold 18129, Random Forest Regressor 27032, Combine the two prob together 29925, gbdt it should be Notice that random search tried gbdt about the same number of times as the other two while Baian optimization tried gbdt much more often 16533, let s plot and analyze the age and survival correlation 43136, CatdogNet 16 16650, Missing Values 5154, go ahead and select a subset of the most predictive features 23883, Almost all are float variables with few object variables 12893, The medians are a little different here although not by much 34753, Vocabulary and Coverage functions 8548, Basement Features 10108, predict a Testing data with our XGB Model 28325, Exmaine the previous application Dataset 12130, Splitting the data back into the original train sub form 1 15011, Passenger Class 27280, Visualize many predictions 19304, Data Preparation 30265, Scikit Learn won t let us set threshold directly but it give us access to decision scores it uses to make predictions 20362, Pearson Correlation Plot 24749, Imputation 35557, Parameters 15984, In this part we scale the numeric features and convert the categorical features into numeric features 7453, Age Column 13893, Data Balance 5904, when to use only fit and fit transform Only fit used below 40412, the latitude values are primarily between and look at the longitude values 16378, categorizing starting from 1 23036, Quarter Analysis 20396, Multinomial Naive Bayes Model 16367, Try Groupby 19582, Concat test into train 34015, January 0 31544, MasVnrType 5193, We can now compare our models and to choose the best one for our problem 30532, Exploration of Bureau Data 19614, Indicator features 14100, Model Comparison 21525, And after the max pooling 39776, create a first simple model that be my baseline model 22273, I choose to fill the missing cabin columns with 0 instead of drop it becuase cabin may be associated with passenger class We have a look at a correlation matrix that includes categorical columns once we have used One Hot Encoding 5163, Bagging boosting 30972, To get a sense of how grid search works we can look at the progression of hyperparameters that were evaluated 32030, we put predicted age data to cells with only missing age values in Age column 22110, Scaling numeric data 26013, Last but not the least Inorder to proceed further in data cleaning and transformations It is always of prime importance to check the distribution of all the numeric variables involved in the study most importantly the target variable SalePrice 32820, Dealing with missed variables 8090, Loading Data 33608, Building Classifier 19529, Applying Function as Filter 28130, MIN DF and MAX DF parameter 9235, Neural Network with Tensorflow 29570, let s try ML now 25675, Feature Analysis 26804, StratifyGroupKFold 1879, Class 11755, Models 6631, Most of the embarkments were from class S 7087, Feature Engeneering 24018, And examples of the wrong classified images 6257, Therefore Fare lends itself to being a good candidate for binning into categories as well 23620, Ensembling 29814, SkipGram Model 6052, Simila distributions but different ranges 41635, Remove Extra Whitespaces 13469, Exploration of Traveling Alone vs With Family 43365, for each prediction there is a vector of 39 probabilities 37093, Categories Display Target Density and Target Probability 37891, Prediction from Linear Model 38479, Class distribution font 17752, Tuning RF parameters somewhat methodically 27369, droping the category name and shop name 5975, GridSearch 7957, Test with higher beta 8393, Creating a new entity Id inside the created EntitySet 27147, Category 4 Location and Style 13087, Decision Surface 16437, Looks like Pclass can help to fill the missing value 7414, We know that Alley FireplaceQu PoolQC Fence MiscFeature are all categorical variables and their missing values occupy over 50 of total 18426, also check if the CV score improved after the stacking from the single models or not 30770, Ensemble learning 27137, Scatterplots 7362, SPLIT THE DATA FOR LINEAR MODEL AND BOOSTS NN 9217, Overall Bivariate Relation 29103, Bidirectional LSTM 13974, Embarked vs Sex 31740, With Gaussian Blur 43039, we are ready to train the model 23424, First we analyze tweets with class 0 40841, max is 3 mean is only 0 41668, The focus of this section be on tuning the following Random Forest hyperparameters in order to prevent overfitting 4343, Dataset summary 22974, Mean sales per week 15385, fill the remaining missing Ages with the mean values 24907, Confirmed COVID 19 cases per day in China 720, One thing to note about this dataset is the lack of data and with it the curse of dimensionality 20956, Evaluating model performance with evaluate method 10965, Adjusting the type of variable 38103, Reshaping and Normalizing the Images 29727, Distribution of target variable 10720, I know what you want next 30091, C O N F I G U R A T I O N 7122, Pclass vs survived 33690, Check if the dates are in US holidays 4852, LightGBM 16766, Look at hte prepared Data 35354, We have total three csv files in this dataset 19836, Exponential transformation 42313, Probabilities Testing Set 10351, Normal distribution doesn t fit so SalePrice need to be transformed before creating the model 30901, After fullfillign the regionidcity let s check what columns are left 22053, Some quick findings 1718, Peeking at Datasets 2659, We have our training data validation data 38029, Logistic Regression 17696, MODELS 29466, Checking for missing values 21146, We have cleaned and scaled data with defined linear correlation 32210, Add lag values for item cnt month for every month shop combination 33889, POS CASH balance loading converting to numeric dropping 5536, Drop Unneeded Columns 9672, Transform the dataset and get it ready for tuning 42006, isin filtering by conditions multi conditions in multi columns 29140, Mutual Information plots 15057, Submit 25453, How d We Do 18007, Indeed the gender is the feature that is most highly correlated with survival among all with correlation coefficient of 0 15724, Decision Tree Classification 18475, It is realistically better to input the median value to the three Nan stores then the mean since the mean is biased by those outliers 28222, Final model s accuracy 23600, I am splitting into training and testing set for now 2951, Fit these best estimators into the model 33032, Using prcomp on the original dataset throws an error 28696, Clearly we have a variety of positive and negative skewing features I transform the features with skew to follow more closely the normal distribution 43000, Here I check number of rows for each ID 27642, But the table of data is not enough as we have to split the label or what we are predicting from the training data or the pixels 36364, Creating pipeline 27647, Creating the Submission 16385, Combining Sex Titles Pclass 42540, Transform questions by TF IDF 32818, Correlation Table of price doc t by methods pearson kendall spearman 13664, Modeling 42053, get group 4919, Looking at Skewed Features 16005, New features Relatives and Age Pclass 38310, Logistic Regression 2445, Correlations 19899, Bottom 10 Sales by Item 9767, Feature Selection 28166, spaCy s Processing Pipeline 20508, The worst toxic train questions 22646, The names are transformed into title 26661, This file contains descriptions for the columns in the various data files 12463, Train and Test sets 14249, Embarked Categorical Feature 42836, We compute the 10 fold cross validation score by using 10821, it is time to predict missing values of Age 22650, Sigmoid 32082, We have 14 continuous variables in our dataset 23937, Verifying if products actually have description 16606, Outliers 21393, Readiness for Submission File 32865, Rolling window based features window 3 months 25728, Image Augumentation 15781, Perceptron 1516, Trying to plot all the numerical features in a seaborn pairplot take us too much time and be hard to interpret 41300, Feature importance 11404, Selecting Multiple Columns 23745, Logistic Regression 17935, Embarked 29422, First we store the target data into a variable 22328, Removing URLs 37916, Evaluation 8303, The best model is XGB in these runs 41227, start applying different algorithm on the train dataset 24694, let s define a single iteration function update fn 15530, Fare 10635, first finish some routine tasks before diving deeper 37833, SVM 41970, Model Building 23528, Below the encoding is applied to every sentence in train 37662, Data loading 41846, XG BOOST 38957, Creating Dataset 43350, The first image in our test dataset is 2 7640, remove outliers 40720, Training 1684, Relationship of a numerical feature with another numerical feature 42325, Converting label into categorical form 3162, The onehot function converts a categorical variable into a set of dummies 14284, Parameter tuning gridSearchCV 5885, Lasso 6042, Create stacked model and make a new submission 23295, Categorical Features 20771, Having obtained our tf idf matrix a sparse matrix object we now apply the TruncatedSVD method to first reduce the dimensionality of the Tf idf matrix to a decomposed feature space referred to in the community as the LSA method 7354, Some feature have wrong format 19196, we have compact small table easy to work with 20081, Worst Sales Item 29112, The data is ready Time to feed it to a Convolutional Neural Network 7709, Target Analysis 13500, Title 20643, Word Embeddings 32139, How to create row numbers grouped by a categorical variable 32239, Always Always Always remember to close the session after you are done with the computations 16226, we drop the columns which we don t require 28078, Unlike the train data there is no missing value in the Embarked column but there is one missing value for fair 2983, Residual plot 42781, Creating the model 37019, Can we split those categories by level 43397, First of all we need some fooling targets 23606, Loss Function 29331, With the PCA values 11116, Split into Train and validation set 43323, Reshape 15754, Modeling 7557, Voting Classifier 12090, Update the model with the second part 35321, Plot loss and accuracy 30284, Active Count 50 22828, Great so all shops id s in the test set are also present in the training set 14825, Embarked 40743, Choosing final Model 9087, MSSubClass and HouseStyle 24912, Time evaluation 31096, GarageCars font 20125, Device model 42417, Outlier Analysis 18914, Age Feature 37746, we can use the dictionary along with a few parameters for the date to read in the data with the correct types in a few lines 30860, In this example we be using the MNIST dataset which is a set of 70 000 small images of digits handwritten 15681, Train first layer 25479, 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 22046, Since our metric is RMSLE let us use log of the target variable for model building rather than using the actual target variable 38953, Configuration 22407, Getting closer 229, Library and Data 27152, Mason Veneer Types Most of the properties are not having Masonry veneer walls and have low sale price 43124, Import ML methods for training the models 28110, Measure the model fit against multiple models 43063, check now the distribution of the mean value per row in the train dataset grouped by value of target 32691, Ok let s now train the model 35502, Fit the Model 29362, Building Model 5045, Not surprisingly most positively correlated features are e 43022, Feature selection Matrix extra need bit more understanding and usage 42455, Checking missing values 35463, Visualiza the skin cancer at Palms soles 41909, Optional only keep images of type 0 and 2 2 being the second most present class in this sample 30520, Target Variable with respect to Organization and Occupation Type 31680, Constructing a very simple neural network to classify our images 4608, Things to note 29780, Visualise Training data 31618, F1 score is precision and recall combined into single metric 22606, Final preparations 34022, Count Atemp 41162, FEATURE 8 DEBT OVER CREDIT RATIO 27458, Handle Capitalized Words 15155, converting Categorical data to Numerical 5398, I tell the missing fare by the PClass Parch and SibSp 8080, Overfitting prevention 36939, Fare 38293, Fit the model 16913, Encode categorical 34240, Load Libraries 29930, for the next four hyperparameters versus the score 20408, Number of unique questions 816, log transform 33894, bureau agg previous application agg application train test 35769, Additional testing 2530, Hyper Parameter Tuning for AdaBoost 1632, Log transformation 12778, Start modeling 27832, Normalization 2321, Fitting a RandomForest Regressor 2421, let s take a look at all the categorical features in the data that need to be transformed 39423, map Sex to 0 for male and 1 for female 5254, Drop Column Importance 42642, Mislabeled Samples After Cleaning 20035, Since this is a multiclass classification problem we One Hot Encode the labels 13229, Pseudo Labeling Technique explanation of semi supervised learning and pseudo labeling c2218e8c769b 37911, Evaluation 31349, Take a look at your submission object now by calling 8088, Deal with predictions close to outer range 13503, Correlation 22276, We fill with the mode of the data column 29017, Age 18433, Creation of the histograms 14713, K NEAREST NEIGHBORS 3033, Model Predictions 40688, NOW WE CAN DO SOME FEATURE ENGINEERING AND GET SOME NEW FEATURES AND DROP SOME USELESS OR LESS RELEVANT FEATURES 9245, Correlation matrix of some selected features 15720, Train dataset 530, Boxplot 13607, When handle missing indicator an indicator column be added 42413, First Few Rows Of Dataset 32033, GridSearchCV returns test scores There be 5 because we use 5 fold CV splits for each parameter combination in param grid 32039, Since we want high True Positive Rate and low False Positive Rate we can set the point closest to 0 1 on ROC curve as the optimal operating point 40853, Transformation of Distributions 430, Fence data description says NA means no fence 42237, Univariate analysis box plots for numerical attributes 33446, LGBMClassifier 938, The 4 C s of Data Cleaning Correcting Completing Creating and Converting 30202, we ve included method anova since it is not a classification but a regression problem 3516, examine numerical features in the train dataset 37888, Elastic Net Linear Regression 21501, plot images from the training set of different conditions 27310, Data Conversion 39439, Model Submission 2300, Changing the Column names starting with numbers later functions sometimes have issues 8382, I due with Nan s later but by now I fill with miss 28367, There are 221 unique words present in training set 25815, it s time to combine them 34048, Deal with the cyclic characteristic of Months and Days of Week 28463, Column unitcnt 3671, Observe the correction 33251, Missing Values 41361, According to this chart we can t say there is a clear correlation between garage quality and price 19300, Data Interaction 10685, MSE 1n i 1n yi yi 2 24255, Irrespective of the class passengers embarked in 0 and 2 have lower chance of survival 307, our scores 19896, Grouping training by shop id and Item id 7108, We use logistic regression with the parameter we just tuned to apply bagging 18560, Most passengers don t have cabin numbers 27323, Model building 14531, Cabin 27157, KitchenQual Kitchen quality 35369, Initialize Augmentations 20826, The following extracts particular date fields from a complete datetime for the purpose of constructing categoricals 12357, TotalBsmtSF Total square feet of basement area 21322, T correlation plot th y m t s nh m bi n c s t ng quan m nh 40669, Bi gram Plots 23676, We can take a look at a few random images 78, Cabin Feature 38013, Aggregations over department 7942, A Deep Learning approach Multilayer Perceptron MLP for regression 16042, Yipee 75 37639, We ll also plot the distribution of predictions 2560, Using the Leader board option to arrive at best model 39958, We stack all the previous models including the votingregressor with XGBoost as the meta regressor 2917, Preparing the datasets for Machine Learning 2908, Fill the Age Feature with median 17652, Voting Boosting models 21581, Show fewer rows in a df 39788, Lets look at distribution of logerrors with top 15 frequent regionidzips 21487, Code for Loading Embeddings 15463, Feature importance 18204, Building the Model with Attention font 24883, Creating An Additional Family Feature 26012, One thing that was pinching me in my was the price movement 36818, for training data 19569, Please note that some lines with coco evaluator are commented out 15723, Gaussian Na ve Bayes 27151, Roof Styles Most of the house are having Gable and Hip roof styles and average sale price of 1 14803, Categorical Variable 40969, Creating new column Daily Revenue 31366, Fold Cross Validation 16870, Residues of train dataset view 25186, Reading the data 28504, We build our model using high level Keras API which uses Teensorflow on the backened 22468, Timeseries 37300, N Grams 9788, Since there are many features it can be hard to understand correlations from the heatmap 42774, Fill Age 20304, We are dealing with 143 types of product 4463, Sex Mapping and Encoding 7598, Boxplot SalePrice for Neighborhood 547, Optimization of Classifier parameters Boosting Voting and Stacking 34488, Write the functions for model optimizer activation and loss 40770, Function used to plot 9 images in a 3x3 grid and writing the true and predicted classes below each image 31236, Checking for correlation between features 2120, Tuning Lasso 23293, Outlier Detection 26181, Catagorical Variables 21771, REVIEW The values is assigned like float 7393, Splitting in training and testing datasets 25673, Improve the model 16345, Create Submission File for Kaggle Competition 15528, Embarked 6451, Prediction on test data of different model 9858, When we add count variable indexes start from 29713, Boxplot allow us to have a better idea about the presence of outliers and how badly they may affect our predictions later 24447, remove stopwords pass to lower add delimiter and more 38132, Those that survived had paid in the fare range of 24982, Filling NaNs in categorical columns using most frequent occurrence for each column 38944, Effect of Competition Distance on stores performance 3930, Checking Models Accuracy 747, Running the CV for the ensemble regressor crashes it get an indication on one regressor 17460, We are going to drop columns that we not use in Machine learning process 1640, My final ensemble model is an average of Gradient Boosting and Elastic Net predictions 43024, its time to turn everything into numbers 32568, Save data with duplicates 40676, let s try doing it for K 9 26568, Change the numbers in the image name to look at a few other cats and get an overview of what we are working with 43306, and after a few hours of trial error I have chosen what appears to be the optimum way to handle nulls in the numerical columns 32526, Train the Model 22286, Submission To CSV 30867, We can get a better sense for one of these examples by visualising the image and looking at the label 32730, item id date block num and month have quite a predictive power 15456, Family Survived 11760, I took some suggestions from the documentation of scikit learn and some other helpful kernels here on Kaggle to tune our Gradient Boosting Regressor and Kernel Ridge Regressor 40480, Perceptron 21127, In this case obviously all dispersion measures are low cause the difference of 4 6 years in comparison to 2000 is small 1103, Feature importance 40241, Or as I like to call it smell the data before dinner 31830, Under sampling Cluster Centroids 41400, NAME INCOME TYPE 17468, Mlle The term Mademoiselle is a French familiar title abbreviated Mlle traditionally given to an unmarried woman 41525, A boxplot is a standardized way of displaying the distribution of data based on a five number summary median third quartile and maximum 40170, The Core Data Science team at Facebook recently published a new procedure for forecasting time series data called 16660, Analyzing Features 14826, Ticket 443, Lets check for any missing values 27517, Modelling 3160, Copy NeighborhoodBin into a temporary DataFrame because we want to use the unscaled version later on to one hot encode it 26450, A negative value means that our model might work better if we do not consider the respective feature 2007, Looks good 8100, SibSp vs Survival 28212, Calculate class weights 13124, features analysis 975, let s create a Light GBM Model 16004, Floors 36589, Use all training data learning rate 42072, Each of the models are tuned using Random Search 41442, Below is the 10 tokens with the lowest tfidf score which is unsurprisingly very generic words that we could not use to distinguish one description from another 17999, Processing the training and test set seperately 7581, Scatterplots SalePrice vs Area features 29835, Load order data 3443, Remember that ultimately we d like to use the Title information to impute missing values of Age 30964, Learning Rate Domain 12789, We extract the relevant feature from the test data as we have done before 20350, I pick 0 23527, Load the Multilingual Encoder module 17449, i like to split the data in a training and a test dataset just to be sure my AIs work 24666, Write prediction 12979, Survival probability of small families is almost three times higher than big families 40775, Run the training and prediction code 30425, Mixup 2 41322, The general metric for MNIST is a simple accuracy score 13283, In machine learning Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes theorem with strong naive independence assumptions between the features Naive Bayes classifiers are highly scalable requiring a number of parameters linear in the number of variables features in a learning problem Reference Wikipedia 41063, Basline Model Multilayer Perceptron 34484, There is a fairly even distribution for each of the digit in our dataset which is actually good since there be no bias to a particular digit when our CNN is trained 11875, Dealining with Scewed data 15001, We have several object columns that we need to deal with Name Sex Ticket Cabin and Embarked 14813, Pclass Survived 15238, Missing values 17802, And let s plot the Fare clusters grouped by the survived and not survived 43150, Preparing Files to be given for training 20446, Go to top font 38427, Data augmentation 22096, Load the Model with the Lowest Validation Loss 7518, Permuation importance of features 41949, Visualization Code 17359, Model Evaluation and Comparison 1734, Gender vs Embarked 26080, You can use GPU to accelerate model training 38719, Here is where we finally train the GAN 1428, Logistic Regression 20122, Feature Importance 27250, Prepare the test set with the date information required for prediction 8932, Remaining Features 9182, YearRemodAdd 6272, Embarked 36820, Preparing the Data 2805, get num feats Returns the numerical features in a data set 9162, GrLivArea 17547, Get data of Passengers with Pclass 3 having 0 Parch and 0 SibSp simliar to the requirement fare null value 6829, Numerical Features 4881, You can skip arguments other than x cmap is styling the heatmap 40931, Callbacks Functions 21937, Python 540, New Feature Title 42722, At first find the int features which have high correlation with target 15869, Test model 8843, For columns with no ordinal relationship we ll do some special processing later 26558, Evaluation 29044, Read image 8778, Embarked And Sex 13721, NOTE Found unmatching titles like Don Rev Mme Ms Major Lady Sir Mlle Col Capt Countess Jonkheer 17358, The next model Random Forests is one of the most popular 28995, Highly Correlated variables 5142, Number of labels cardinality 27093, to easily test the blended model first I saved all the models already fitted so I could retrieve the models without running all the process again and also to save modifications made on the models 14885, Numeric Variables 32877, XGBoost 15015, Passengers from Cherbourg have the higher probability of surviving 3214, Checking the learning process 18207, Calculate OOF AUC font 27329, Make a submission file 10592, Gradient Boosting with params 663, Naive Bayes 40945, now let us prepare five learning models as our first level classification 32356, Predictions 9678, L1 and L2 regularization 18192, expanding the aggregate 8314, Imputing the missing categorical value with the most frequent value 13577, Seting Cabin into Groups 20083, Worst Sales Shop 21811, Interest Level 25204, Submission 3593, Ridge 41023, we submit the predictions to the leaderboard at the time I first tried doing this the old leaderboard with only 50 of test data was still in use 28155, Load Dataset 22791, Import Libraries 4386, GarageArea feature look uniform distribution and linearly correaltion with target SalesPrice 27303, Now without the most popular product ind cco fin ult1 40309, LSTM models 3667, Splitting the data into categorial and numerical features 9688, Coorelation matrix and removing multicollinearity 28158, Train Model 26240, Pseudo Labels for Model 2 18326, Using best lgbm model 41263, The administrative regions and municipalities are distributed as follows 32095, How to stack two arrays vertically 24719, Show Model Accuracy as function of num PCA components 4388, Most of the home doesn t have pool 39179, apply it to some predictions 39193, Descartando colunas que n o utilizaremos 6018, Hasilnya memang overfit tapi ingat ini hanya base model saja lagipula perbedaannya hanya 1 2 saja di tingkat akurasi data validation dan test 1080, Modeling 17888, Getting title from name 18194, Similarly there are products with 0 sales and only returns 1894, SVC Model 2762, Usually Categorical Variables are imputed with mode but it won t make sense in all cases so in order to make them loaclized based on Neighborhood and we can impute the data 39738, Ticket 12215, The grid search looks like this 39877, stripplot It is easy to understand the overall appearance when there is not much data in data set 22464, Waffle chart 5115, Missing Value Counts 13978, Preprocess Name 41339, Categorical Features 10628, Split Train and test Data 3442, It s reasonable to put both of these in the Mrs category 29715, Caution Correlation matrix won t help us detect non linear or multi variate feature relations though 16575, Creating New Features 22962, Examples of hair augmentation with TensorFlow 21353, Specify Model 23318, Add previous item sales as feature Lag feature 39228, Lets remove correlated features from Santander database 2455, We then use the transform function to reduce the training and testing sets 29906, Convolutional Neural Network 33250, Categorical Features 24118, Final prediction 21792, Logistic Regression 16922, Random Forest Best Performing on LB 5895, Label Encoding these 10335, Imputation of Missing Values 14672, Hyperparameter Tuning 15299, Support Vector Machine Model 19661, DFS with Selected Aggregation Primitives 20605, Age 10127, Building Training and Validating our Models 22355, Using xgboost XGBRegressor to train the data and predict loss values on the test subset 14311, Writing the Prediction 6151, CatBoost 10785, I want to set index as PassengerId 26905, Create Submission File for approach 10 33693, Moving Average 14127, Here in both training set and test set the average fare closest to 80 are in the C Embarked values 26511, when all operations for every variable are defined in TensorFlow graph all computations be performed outside Python environment 40447, Summary 21897, Parameters 205, Libraries and data 23451, From this we can conclude that the registered are mostly people going on their jobs which explains the peaks at the start and end of office hours Clearly these people would have a more definite and predictible schedule and are therefore more likely to be registered In order to test this hypothesis we plot some more graphs 16950, And voila We trained the model its time to save the predictions 21262, Check Item Similarity 567, Ada Boost 41491, Use the new training data to fit the model predict and get the accuracy score 10729, Basic logistic regression 21328, Garage 20136, Values in grayscale range from 0 to 255 25672, use the mean absolute error function to calculate the mean absolute error corresponding to the predictions for the validation set 30663, Cleaning and lemmatization 3412, look at FamilySize in more detail 40958, we need to check for the data type as we can only enter ints floats to our model 9287, New Features 35588, create the final input array with 42000 images each with size 75 75 3 37632, This early stopping implementation is based on the following three implementations that I found 1624, Linear Regression without regularization 25882, Histogram Plots of number of words per each class 0 or 1 13506, Train Score 37217, We can replace Quorans with Quora contributors 12099, Replace NA missing values by most often in column only for columns with 2 and less NA values where do not make sense to invest hugely into Analysis 4421, Make final prediction as combination of Ridge and Random Forest 11171, We use the scipy function boxcox1p which computes the Box Cox transformation of 1 x 10935, Dropping the columns with highest percentage of missing values 30138, TFBertModel 32051, We are all set 10307, Clean the data 35632, Our digits best friends aka Nearest Neighbors 40882, now we have OOF from base or 0 level models models and we can build level 1 model meta model We have 5 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 17528, Extract titles from the Name property by using regular expression 6054, Basement 31648, MODEL 4 GRU 17671, Analysis 35378, Modelling 38552, Checking the bad word features 28272, Visualizing a digit from the training data as a 28 X 28 image 23480, Checking your predictions 12378, Bar Plot of all categorical data fields 43198, Create submit file 36974, Feature importance 2750, The two lines can be merged into a single line of code as 2385, Several ROC curves in a single plot new in sklearn 32635, Formatting for submission 24986, Removing numeric variables that are highly correlated 28095, Forming Model 10812, I am not sure 4417, Ridge Regression with Cross Validation 41042, In order for the notebook to run on Kaggle scripts we subsample the training data 22926, It looks like the bulk of titles are either Mr Mrs or Miss which are the standard titles and the rest are what I call special titles 33771, Normalization is performed on the Dataset to Scale the values within a Range 21759, Missing values in Antiguedad 9126, Alley 35415, Make Predictions with Validation data 9586, Scatter plot 4442, By simply adding a log transformation my place in competition jumed almost 2000 forward 19125, Feature interaction 10556, MDS Plot 37204, Averaged base models score 6407, many Null Values 16251, Training 19548, Bag of Words Countvectorizer Features 14742, There we have it 23652, lets quickly make EDA with just one line of code for that we have we have dabl which tries to help make supervised machine learning more accessible for beginners and reduce boiler plate for common tasks 8018, Survived 35187, Visibly two major clusters are there 15111, The Cabin feature itself as it stands now doesn t really provide all that useful information 15074, Name Title 36671, let s tokenize these messages 7139, Family Size 31077, Looking at the Kdeplot for GarageYrBlt and Description we find that data in this column is not spread enough so we can use mean of this column to fill its Missing Values 17703, PREDICTION ON TEST DATASET 28593, FireplaceQu 8414, with our best predictor we can cut only two outliers use it and substitute all others bath features with a existence indicator 12909, Check the shape of the datasets 11141, for r squared calculations we need to limit the comparison to only the train data since the test data does not have a Sales Price to compare to 24299, Reshaping and Scaling data 13748, Create Feature Engineering 15739, We can create new fature from NAME 43210, Using CNN 302, gotta encode all the object types 13158, Label Encoding AgeBin Class 7891, I explore the effects of the other featuers independently 12013, Desicion tree is a simple tree based models which divides the predictions data into several desicion boundaries based on some set of conditions and approximate the predictions on new data 4679, begin with the most correlated feature OverallQual 20680, Evaluate the Model 6755, Checking Skewness for feature 3SsnPorch 24147, Reduce the Problem Size 502, As indicated by the Kaggle information word reference both SibSp and Parch identify with going with family 38470, Download and Preprocess 2260, SibSp and Parch 19831, Gaussian Transformation 10767, LightGBM 24011, FNN 38472, Model Build Train Predict Submit 22765, look at the cleaned text once 36746, Concatenate daysBeforeEvent feature with our main dataframe dt 7286, GaussianNB Model 18768, This is a simple 3D CNN architecture for classification 38023, what are insincere topics where the network strongly believe to be sincere 1050, Log transform skewed numeric features 42561, Sigmoid function in plain numpy 15235, Prediction 26988, Run model 7864, The first step is the detect in which columns there are non valid values 1532, Looking at the Test Data 7676, Feature engineering 21536, Decision Tree 27977, violinplot 19911, Revenues data featuring 34176, Visualizing Layer 3 9260, Moving ahead to another variables 29904, Cross Validation 38987, Training Model 31713, Fatalities 22608, xgboost 25590, RandomForest 32542, check any missing value 6673, Random Forest Classification 30389, Train Test split 4693, Here skewness is our enemy since we gonna work with linear models 14290, Understanding Feature Importance 6789, Cabin 17899, Lets look at the Feature Importance plot 7849, I recently had to deal with multilabel data and had the same problem and to overcome this I made a fairly simple funtion for chain classification and you should also be able to create your own functions and classes 32787, Binary Encoding 13873, We fit and predict on the best SVC model that we derived based on the scores 12786, Compute Predictions 20099, Christmas flag 18975, Display more than one categorical variables distribution in a parallelized view 1864, XGBoost 3971, View Model Performance 23506, Add image augmentation 27598, Adding Hidden Layers 30971, since we have the best hyperparameters we can evaluate them on our test data 6338, We split them to 11787, Name Title 17800, And let s plot the Title grouped by the survived and not survived 36796, Even more you can do things like get the definition of a word 15577, Categorical features 26686, Term total credit annuity 24456, ABSTRACT from the Paper Edge Based Color Constancy 40976, Total Daily Revenue 15366, Dropping unnecessary columns from the dataset 37444, Tensorflow roberta model font 3999, We write a function that take pairs from the array with errors many times and at the end calculate the expectation 4233, Data Model Selection 15638, Select Columns of Interest 21149, We had some fun with LASSO penalty parameter adjustment 9352, Generate new input files for the training and test data with predicted age 11672, It looks like passengers that travelled in first class were more likely to survive 24843, Model Ensemble 18057, It s time for making our prediction 24140, Logistic Regression Model 28758, Residual Plots 13344, Creating and normalizing matrices for our model div 40059, use the last dev dataset to yield some insights about predictions and weaknesses of our model 7039, Type of alley access 4285, Sale Price Over Time Period 15816, from this we can inferred that the survival rate decreases with the class 37407, Since the LightGBM model does not need missing values to be imputed we can directly fit on the training data 2893, Below we have applied BoxCox transformation for each numerical features when we input the feature values to boxcox function it return lambda values which we use to transform each non gaussian distribution to gaussian distribution 29058, Template image 8224, As understood from the target column the box plot depicts some of the outliers 21627, Pandas datetime lot s of examples 30251, Convert the dataframes into XGB readable format 25814, let s add more penalty when our argmax prediction is far away from our target 32633, or to tune hyper parameters 30631, Relationship between wealth and survival rate 12903, We do a grid search to find the best parameters 296, Cabin Age and Embarked have missing values ranging from 0 9835, Random Forest 858, Of all passengers in df train how many survived how many died 36633, let s remove skewness using BoxCox transform 37521, Parch Survived 12783, Train Test split 19574, handle item price outliers 26281, Data preparation for Machine learning 36376, For the most part our labels look to be pretty evenly distributed 34960, Plots 9746, Fare 6394, Missing Data in percentage 2657, Convert all the data into numeric form 27156, Heating Type of heating 40395, Fold 2 7711, To reduce the skewness we ll take log of SalePrice 2746, If we find that most of the values of a column is missing we might want to go ahead and drop the column altogether 36449, Model generation 3287, Combine these models for final prediction on test set 41004, confirm our classifier block matches Pytorch s resnet implementation 37645, manager id 14762, Logistic Regression 18843, LDA Implementation via Sklearn 9166, Garage Cars 17385, we can compare the performance of the main model lin clf and the pseudo model as follows 23742, Random Forest Classifier 23532, Here I follow The idea is that some keywords with very high probability signal about disaster tweets 25451, CatdogNet 16 21094, Model performance 28227, Below we juxtapose the original input image with the correponding generate image from the neural network 36217, 259 missing values for LotFrontage we use SimpleImputer to fill them with averaged values 16456, We take log transform 16459, People travelling alone are likely to less survive 1811, Creating New Features 2347, CatBoost 8318, Performing cross validation of different models on 5 folds of training data 12044, For example let s look at scatter plot of SalePrice and Lot Frontage 797, Gini Impurity 35380, Data Preparation 1846, Significance of Discrete Numeric Features for SalePrice 26683, split categorical discrete and numerical features 17750, Converting categorical variable labels 32038, Since our dataset is not imbalanced we can use ROC curve to find the optimal threshold we compute the area under the ROC curve ROC AUC to get an idea about the skill of the classifier In case of highly imbalanced datasets it is better to use precision recall curves 8883, The 4th feature which we be adding is the Season feature 38640, Data Gathering 30575, To make sure the function worked as intended we should compare with the aggregated dataframe we constructed by hand 9090, This leads me to believe that I should have a column for houses that have 1945 Newer styles 37401, Admit and Correct Mistakes 42851, Data visualization 21064, Defining Model 40166, As mentioned before we have a strong positive correlation between the amount of Sales and Customers of a store 22637, Model 2 Mean Model 21530, look at the connections for the first 100 rows of positive responses 17637, Age grp Fare grp 35759, Base models 9220, Train the KNN 28741, Getting the best parameters 11135, lets use a log y scale 1, Based on the correlation heatmap in the EDA section its very clear that LotFrontage is correlated with LotArea and LotFrontage 10235, check one more time for missing values 33893, agregating POS CASH balance features into previous application dataset 15044, Name 30922, Real values of testdata 18696, create a ClassificationInterpretation object 42005, filtering by multi conditions in a column 33267, Set how is our prediction 17344, KNN 3493, Model 4 Gradient Boosted Tree Classifier 38524, We shall take the maximum length to be 150 since we shall be concatenating text and sentiment columns There is a very helpful function called encode plus provided in the Tokenizer class which can prove to be real handly It can seamlessly perform the following operations 15921, Model Training and Selection 23948, Converting all the categorical columns into numerical 19165, SVD on tf idf on unigrams for iten description 36578, On the other hand the age distribution is similar for both genders 13128, Survival by Pclass 94, Age and Survived 16537, combine the Parch and SibSp feature to form a better Family size feature 5304, Partitioning a dataset in training and test sets 15565, It is also natural to assume that Bowen Miss 19703, take a look at the distribution of the target value 26998, Some characters are unknown 10220, As there is no direct releation between Embarked and Survived variables we can drop this from our feature list 1834, LotFrontage NaN Values 12084, Distribution study 14640, make sure there are no null values left 18997, Make a submission 19265, LSTM for Time Series Forecasting 3981, Get target 28626, ExterCond 24546, Number of products by seniority group 31214, Target Column 28211, Understanding useful metrics 6686, Zoomed Heat Map 14650, Missing Ratio 2645, There are 177 NaN values in Age 686 NaN values in Cabin column 24246, Gender Sex 4246, We can optimize Scikit Learn hyperparameters such as the C parameter of SVC and the max depth of the RandomForestClassifier in three steps 2198, Applying the outliers to Age SibSp Parch and Fare columns 19593, item and city code 6191, Models without Hyperparameter Tuning 41209, len prediction len GT 8065, SalePrice per square foot 23910, Table for scores 12658, Validate with KFold 1709, There are a lot of missing values and some of the columns like Xylene and PM10 have more than 50 of the values missing 36047, Italy 36046, Classmethod and staticmethod 16724, SibSp 30077, We only used a subset of the validation set during training to save time 26880, Include only numerical columns and impute columns with missing values 977, Learning Curve 17366, we use the classifer that did the best job to make predictions on our test set 35165, Plot the model s performance 19127, Statistics 9244, Grouping the data 6416, Lets handle Skewness before moving to Bi Variate Analysis 2679, ANOVA for Regression 14602, Logistic Regression 7568, describe for categorical features 31063, Domain 18694, That s a pretty good fit 15242, Fare 1351, We can not create FareBand 43319, We have to impute the missing values 38816, Inference 13289, We tuning the hyperparameters of the LGBMClassifier model using the HyperOpt and 10 fold crossvalidation 11393, we have two missing values in Embarked and one in Fare 36732, Importing models and other required libraries 1613, One Hot Encoding the Categorical Features 40697, NOW RANDOM FORETS REGRESSOR GIVES THE LEAST RMSLE HENCE WE USE IT TO MAKE PREDICTIONS ON KAGGLE 14359, Crosstab and FactorPlot Survived vs Pclass 7301, Observation 1739, Notice that for Parch people travelling with 3 4 5 6 parents children aboard are very little 42172, We can obtain the number of axes and dimensions of the tensor train images from our previous example as follows 1659, Another categorical variable it looks a bit complicated 2744, Just as isnull returns the number of null values notnull helps in finding the number of non null values 18356, regression tree 1329, Correlating numerical and ordinal features 8737, Living Area 4416, Splitting Data back to Train test 18305, item cnt day features 37478, TF IDF stands for term frequency times inverse document frequency 15340, Making and Printing our predictions 39108, Sex 37711, Data Augmentation small important step small 16908, fillna Age Child 15776, ML models 10689, First visualization 5118, Data Preprocessing 25757, Those start looking really similar to each other 19465, Imports and useful functions 15291, Creating categories based on the Title of the passangers 11073, Models 26021, There are some variables that I came across are categorical values 7372, I concatenate all 3 tables and for convenience reset the index using the parameter ignore index True 43168, PCA 39740, Well that doesn t look promising 1702, Finding reason for missing data using a Heatmap 4820, Normality Assumption AS already mentioned we ought to make normality assumptions when dealing with regression 8233, I divided dataset into 70 30 ratio so that we can test our scenarios 24763, Kernel ridge regression 16635, Missing values 15655, MLP 1223, Encode categorial features can and should be replaced 40046, now chose a model structure 42110, Creating Prediction 41911, Making sure fnames and labels are in order 40052, If you like to search for optimal min and max learning rates just choose your values and set find lr True 33232, As the next step we pass an image to this model and identify the features 35764, Kernel Ridge Regression 39087, Searching for optimal threshold 16119, Decision Tree 2289, ROC AUC Curve 8621, GrLivArea vs SalePrice 514, Accuracy Comparison through Plots 25877, Word Frequency 18137, Loading data 16873, Sex vs Survival 37157, Predicting the activation layer feature maps using the img tensor below 28778, look at the distribution of Meta Features 24538, create income groups 26415, These distributions for the real fare per passenger now strongly correlate with Pclass and look more natural 35153, Plot the model s performance 2824, optimizing hyperparameters of a random forest with the grid search 2833, Model with plots and accuracy 19555, In order to avoid overfitting problem we need to expand artificially our handwritten digit dataset 14554, Embarked font 27967, Reducing memory size by 50 20146, find out which features are highly correlated to sale price 25000, Renaming the categorical columns in the format feature name category name 10856, Extracting the column names of the numerical and categorical features separately and filling the rest of the missing values 37319, First select the first layer filters parameter 35137, Is there an increase in promo if it is a School Holiday 12756, Scale our data 32059, This returns an array containing the F values of the variables and the p values corresponding to each F value 27080, Thus we have converted our initial small sample of headlines into a list of predicted topic categories where each category is characterised by its most frequent words The relative magnitudes of each of these categories can then be easily visualised though use of a bar chart 36681, Model Training 13557, Fare mean by Pclass 24372, Max floor 18413, Training Evaluation and Prediction 11940, We are trying to remove every Null value with the best possible alternative 7754, Log Transform 16519, No of female survivor is much more then the male survivor 18516, Check for null and missing values 12055, Linear Regression 30533, Exploration of Bureau Balance Data 11280, Collinearity can happen in other places too 40139, Kick off the training for the model 109, family size 34263, Normalize 35091, True Positives TP 20110, Item count mean by month city for 1 2 3 6 12 lag 21026, Modelling 546, Standard Scaler 15200, Family Size 7374, Before I start extracting surname and name codes note that in the Kaggle dataset the title of Mrs 37356, PClass vs Survived 1276, Feature Embarked 3 2 4 12651, to tune all the hyper parameters 19621, CNN MODEL 14707, OBJECTIVE 2 MACHINE LEARNING 31689, Plotting actual class with images 1725, Age Distribution 17711, Another one thing People with family size more than 7 didn t survive 12527, get started 19639, For now just drop duplicates 17020, Create cabin type feature 22669, Most Common Bigrams 33744, A Typical CNN structure one CNN layer 21796, Random Forest 20556, Model compilation 32102, How to make a python function that handles scalars to work on numpy arrays 10596, LightGBM 31065, File Format 36063, Predict Monthly 11304, Ensembling 32952, plot the distribution of difference between public score and private score 32571, Domain 11322, Sex 40438, Creating Callbacks 5076, How about log transforming the skewed distribution of SalePrice Will we get a better score 39258, This category covers roughly between and of all realisations in the dataset 33284, Family Survival Detector 4546, Concatinating train and test data 9218, K Nearest Neighbors based Model and Prediction 30992, First we test the cross validation score using the best model hyperparameter values from random search 15177, Update dataframe with Age 31611, RANDOM FOREST ALGORITHM 40007, Insights 410, Random Forest 29859, List out all the data elements containing the specified string 29921, We can do this for all of the hyperparameters 298, Getting the data 33157, Alternative explanation 37609, Filling Missing Values 3737, Will use a simple logistic regression that takes all the features in X and creates a regression line 9648, Looks good and well defined for different numbers of rooms except the one with 11 rooms 41784, CNN model With Batch normalization 4996, We note that the distribution is positively skewed to the right with a good number of outliers 2323, Generating a Confusion Matrix 35335, Compiling the Keras Model 6937, Visualize correlation coefficients to target 2301, Pandas Check for NA s in a column 8073, impute all incongruencies with the most likely value 34701, Averaging the extrapolated values 16976, Split them to train and test subsets 18077, The maximum area of bounding box 16245, Submitting the predictions 31999, that our model building is done it might be a good idea to clean up some memory before we go to the next step 33285, Title Extractor 570, VotingClassifier 2112, It looks we found something the bigger the house the more it costs but the bigger the rooms the less the house is expensive 8433, Lot Frontage Check and Fill Nulls 42553, ROC 7830, Jointplots 33797, By itself the distribution of age does not tell us much other than that there are no outliers as all the ages are reasonable 33073, Submission 40646, Make Data Model Ready 23572, It looks like our model predicts reasonably well 14877, We can also use the x bin argument to clean up this figure and grab the data and bin it by age with a std attached 916, Deviate from the normal distribution 24475, One hot encode the label values digits from 0 9 8064, MSZoning 12885, Gender and Embarked 23368, Some of the Correctly Predicted Classes 12075, Avoiding Multicollinearity 14767, Our Logistic Regression is effective and easy to interpret but there are other ML techniques which could provide a more accurate prediction 26667, previous application 21816, Together 4185, Imputing missing values 18753, We can also rename a column as follows if we wish to 2470, Adding Family Size 30864, Reshape 233, Libraries and Data 38020, Finish 32397, Here we make predictions using the model we trained 9750, I decide to fill missing data in Age and Fare by median value 26045, We can also create a small function to calculate the number of trainable parameters in our model in case all of our parameters are trainable 22177, do some cross validation to check the scores 41268, Diverging Colormap 29383, ol DATA PREPROCESSING span nbsp nbsp 15172, This is our accuracy score 23212, Evaluating Different Ensembles 560, Random Forest 2461, SelectPercentile 12437, First pipeline with xgboost 32348, Treating text lowers the length of texts and therefore allows us to make a model with less parameters and a shorter training time 13495, Embarked 691, Training the model and adjusting precision 20570, Family Size denotes the number of people in a passenger s family 4530, NaN 5 Pandas fillna NaN NaN Age Fare 8377, Drop em up 41418, Split train validation set 14834, Ensemble Modeling 24521, I check customer distribution by country 5252, Embedded Feature Selection Selecting Features From a Model 632, Family 33840, Bar Chart 7570, Barchart NaN in test and train 27830, Check nuls and missing values 11295, One Hot Encoding for categorical variables with no order 7810, Evaluate Bayesian Ridge Model 11500, Transforming some numerical variables to categorical 23746, Tuning HyperParameters 14918, Ticket 36059, Feature Hour 28674, Utilities 37906, Light GBM Regressor 37705, Looks like numbers but a little different than before because here we used much much more data 21671, Catboost 16328, SibSp Parch Feature 8667, we can get rid of empty values in cols 28377, Prepare for Submission 16931, about half of the passengers are on the third class 16954, Age 26091, Adding Callbacks 19219, Naturally we have more data in the first data set because it s possible no changes in a product 34821, Deep Convolutional Neural Network is a network of artificial neural networks 31903, Build the model 25189, visualize how are images stored in matrix form 16706, try to create a fare band 5015, De duplication 2308, Pandas how to add columns together numeric 34693, Mean item price over all shops and months and current deviation from its value 36669, Woah 910 characters let s use masking to find this message 8348, Survival by Sex and Age 1549, We then load the data which we have downloaded from the Kaggle website is a link to the data if you need it 23342, Modeling 14886, Data Visualization and Missing Values 19286, Write output to CSV file 12600, Random Forest using grid search 17349, Voting Hard Soft 18586, 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 17399, Title wise Survival probability 38590, Splitting the data into training and validation sets 18909, Cabin Feature 18580, Here we import the libraries we need 36436, Check out where my output files reside 15672, Gradient Boosting 43253, Importando arquivos 7898, When dropping the colmuns in the train dataset it would be neccesary to do the same in the test dataset 37377, Very well 33864, Backup data 8606, Lets move on to checking skewness found in the feature variables 30637, We should replace rare titles with the more common ones and create a category for high status titles 15905, EDA Relationships between features and survival 11704, Load Data 27320, Creating more custom images by using Data Augmentation 20084, Top Sales Item Category 1041, Encoding categorical features 19369, Frequency distribution of attributes 7277, Title Feature 40294, Start our CNN model 11895, In here I use log function to process the target SalePrice 25896, Topic Modelling 31429, Checking Formula 21857, Normalization 35168, Compile 10 times and get statistics 38202, Submitting the Test Predictions 33654, Correlation matrix 41238, Random Forest classifier for non text data 7357, EDA and preprocessing 15708, Correlation matrix 12391, Testing set 955, Creating NumPy arrays out of our train and test sets 4525, Trying XGBoost 5933, Finding skewed columns 41049, Days Prior Order 29336, With the PCA variables 2335, Train Test Split 41006, Creating Resnet34 model 3034, Submission 8373, KNeighbours 39309, TRAINING FOR PREDICTION 28486, We can reduce memory for columns only having type int and type float or columns having numeric values 31392, We can assign each embarked value to a numberical value for training later 2021, Here we average ENet GBoost KRR and lasso 18723, let s export our model as a pickle 16534, let s plot and analyse how the Passenger Class affects survival chances of a person 3508, Construct the best model fit it make predictions on the training set and produce a confusion matrix 15210, Feature selection and preprocessing 18423, save the oof predictions here as well 40257, Overall Quality 16024, Actually we can construct useful feature from Name and Ticket 24142, Multinomial Naive Bayes 36900, Investigating false predictions 990, gotta tune it 12556, Decision Tree 2089, Before getting insights from the data let s take the final step of the instructions that came with the data and have a general cleaning 1962, now every thing almost ready only one step we converted the catergical features in numerical by using dummy variable 6729, OverallQual Vs SalePrice 20907, Create checkpoint callback 8425, Identify the Most Common Electrical 39006, Function to generate random minibatches of X and Y in synch 5041, LotArea looks relevant and is one of the most skewed features 10782, Uniting the pipelines 9709, Feature selection using Lasso This is done just for demonstaration purpose 6014, Automl yang ada di Jcopml hanya perlu memisahkan data numeric dan categoric 29847, drop the coulmns that have more than 20 missing values 31780, Convert categorical features 18595, 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 4698, Among the missing features there is one that is difficult to manage 30579, The sum column records the counts and the mean column records the normalized count 14847, Name 13040, nd approach to treat the Age feature 10991, Below parameters are set after grid search 11765, Our score improved to 16939, the most important features are if you are a man or not how much you paid for your ticket and your family size 8833, Model Creation 10098, check the null values fill it 11064, Filling missing values in both the test and the train data from those calculated from the training data 5011, Categorical Features 7055, Miscellaneous feature not covered in other categories 37366, we can say that fare is correlated with Passenger class 20649, Multi Channel n gram CNN Model 34386, Seasons 56, Maximum Voting ensemble and Submission 2003, That looks much better 32231, The special thing about TesnorFlow is that you can define operations and computations as variables 1280, Features SibSip Parch 3 2 5 10602, Step 4 Define Model Parameters 23420, Before we begin with anything else let s check the class distribution 753, Obtain the Latent Representations 41518, Output data for Submission 37613, Creating features 2483, How many Survived 20599, Walk Through Each Feature One by One 8614, Even after doing this we still have so many feautures which are filled with null values and luckily their missing percentage is low 25207, Analyze Overall Quality OverallQual highest correlation with SalePrice 5145, Separate Dataset into Train and Test 29050, A value make the image darker while values makes the image brighter 18095, For EDA on image datasets I think one should at least examine the label distribution the images before preprocessing and the images after preprocessing 3677, Creating new features 27228, Extract the intermediate output from CNN 41917, Improving our model 24024, Some conclusions that we may make from the plot below 1693, Listing unique values in categorical columns UniqueValues 13900, Insights from graph 780, Models include 40710, Preparing Training and Validation data 9981, plotting catogrical values with repect to their count 33024, CNN 4601, Pool 5880, ANOVA test 29744, Submission 8378, Add non AI knowledge 4870, Combining all area features to a single feature 41055, Cross Validation Scores for the different data sets using Gradient Boosting Regressor 11390, Cleaning and imputation 18924, Creating Submission File 36700, Model Summary 26023, Practically the larger the area the more the price of the property is As in our data Garage and Area related variables have significant relationship with SalePrice Target Variable I came to know that it would be good to make a feature engineering on that attribute 10037, Look thier is 1 captain 1 Don 2 Col 2 Major 7 Doctor in data set 28241, Ensemble modeling is a process where multiple diverse models are created to predict an outcome either by using many different modeling algorithms or using different training data sets 37572, ID variable 19152, Check the distribution of TARGET variable 4099, Age is not integer 10068, Amount of survived small families are considerbaly higher with respect to large families and singles 10626, Missed manipulation of columns code SibSp code code Parch code 3715, BsmtCond BsmtQual FirePlaceQu GarageType GarageCond GarageFinish GarageQual 33607, since we are doing multi class classification we have to one hot encode our labels to be able to pass it through our model 4526, Using XGBoost for stacking also 6079, Name 7285, Gradient Boosting 23260, Bivariate Analysis 5208, We can check the score of the model on the x test before predicting the output of the test set 34606, Fit the XGBoost model 26315, Regression Evaluation Metrics 35357, Creating Sample Submission File 38983, Build and evaluate Neural Network 36412, Separation of Continous numerical features and discrete numerical features 26217, Recall we are using our chosen image as example for convenience I remind you of the chosen images image matrix and its bounding boxes coordinates below But there is a caveat here my bounding boxes array is of shape N 5 where the last element is the labels But when you want to use Albumentations to plot bounding boxes it takes in bboxes in the format of pascal voc which is x min y min x max y max it also takes in label fields which are the labels for each bounding box we still need to do some simple preprocessing below 20276, class weight 0 7547, KNN 24284, Producing the submission file for Kaggle 15261, Observation Female have higher chances of Survival 8550, Other Categorical Features 35908, Visualizing Test Set 40090, Regressors with Dimensionality Reduction 21634, See all the columns of a big df 15327, Lets convert our categorical data to numeric form 8011, try a gradient boost with a weak random forest also we try to find number of estimator with early stoping 600, Interesting 1170, look at the basements 39099, Linsear Write Formula 27119, We only have half of Fireplace quality data 1237, Defining folds and score functions 27011, F1 Scores 29626, Data preprocessing steps is very exhaustive and consumed a lot of time 30844, Crime Distribution over the Month 11440, Ridge RMSE Min Alpha 19294, Model DenseNet 121 33860, Fitting a random model 19969, Interesting 23464, Splitting data into train and test sets 944, Model Data 98, This facet grid unveils a couple of interesting insights 8893, Lasso LassoCV 19001, Model fitting with HyperParameter optimisation 30101, Model Train 28877, Multi Dimensional Sliding Window 18205, Train Schedule font 1341, create Age bands and determine correlations with Survived 27131, First step we separate discrete features 7698, REGULARIZATION RECAP 40739, Baseline Model 12335, Fence 23575, Define the model for hypertuning 11498, Check other features 13330, let s store in 2 new columns the information about babies 15434, let s have a look at probabilistic Gaussian Naive Ba classifier performance 41599, Train the model 7021, Rating of basement finished area 429, Alley data description says NA means no alley access 18082, let s plot the images with large areas covered by bounding boxes 24465, Original 34937, Check it 31844, We think that category and are same as 29187, Random Forest 5147, For numerical variables we are going to add an additional variable capturing the missing information and then replace the missing information in the original variable by the mode or most frequent value 17373, Embarked Q 8235, Basic Bivariate Visualization code 14403, Filling the missing Fare value in test set based on mean fare for that Pclass 5598, OverallQual causes different SalePrice where having same GrLivArea We have to put a strong attention 39097, Automated Readability Index 42881, Containment Zones in India 20106, Item count mean by month main item category for 1 lag 34856, Automatic feature selection 26477, Add ons Pytorch Implementation for Resnet Finetuning 9336, We now have a sparse matrix of the dimensions of number of rows times the number of unique Neighboorhoods which is very close to what we obtain by using get dummies and we have a loss in interpretability given the fact that the matrix looks like this 38660, Count of Parents and Children 27319, Hyperparameter tuning using keras tuner 23519, Cut tails of longest tweets 9634, Dropping ID from the dataset 8344, We train a linear regressor for this new training matrix and predict our target We use Lasso GLM to avoid overfitting 18263, Apply model to test set and output predictions 41963, Submission 8814, DEALING WITH MISSING VALUES 14095, Decision Tree Output File 16679, Data Wrangling 40670, Tri gram Plots 889, Decision Tree Classifier 18302, one transaction with pric 0 2434, for the Ridge regression we get a rmsle of about 0 7367, I also add the column Class to the DataFrame wiki1 and assign it to 1 33882, converting categorical features to numeric by frequencies 10024, The procedure for the training part may be described as follows 36287, PCA Principle component analysis 15184, Learning Curves 16786, Simple Pipeline do it in a different way 17284, You can slice the list of columns like a usual python list object 35945, XGBoost 33259, Show Image 40051, Doing the resize preprocessing in advance is definitely speeding up the computation If you are using more images than the original data you should consider to do so as well 40318, Analyse Tweet Entities 35385, RANDOM FOREST 34408, Processing data for saving 11104, Identify Features Highly correlated with target 29528, PassiveAggressiveClassifier 7049, Exterior covering on house 4551, FireplaceQu data description says NA means no fireplace 5591, Checking the correlation between features 26888, Score for A4 17688 39783, Logerror vs nearest neighbors logerrors 31545, since None is maximum so replacing with the maximum one 32828, build a mdel building function in which we input the layer bert layer and get the model as an output 10228, start with moving target and feature variables 41997, Sorting columns w ascending order 12339, All 157 NAs in GarageType are NA in GarageCondition GarageQuality and GarageFinish 14356, Distribution of Classes of Survived passengers 43014, RandomForest 32896, We have to do the similar score calculation for words in Question2 32063, decompose the dataset using Factor Analysis 20748, as skew value imporver after regularization so we do log operation 36727, Train the network 9438, Correlation Heatmap 39052, Name 4714, let s handle the missing values 26868, One important thing to do here visualize some input images 42057, Making a function for drawing graphs 2314, manually use dataframe methods to split X and y 8678, Check for Missing Values 11650, Gradient Boost 27770, Improve the performance 2810, Bar Chart 17403, Model Comparison 4179, Both Age and Fare contain outliers 27215, because of class imbalance it s better to use focal loss rather than normal binary crossentropy You can read more about it here 36300, Random Forest 34476, Neural Network 15967, Age Feature 45, Ticket column 6369, ENSEMBLE METHODS 6492, Transforming skewered data and dummifying categorical 11296, Modelling 20218, Model Architecture 14353, Number of Survived and NonSurvived with Gender 17695, FEATURE SELECTED 29767, We perform the same operation using the optimal model 29843, clean and standerize the numerical data 26435, This way we can easily extract the feature names after the encoding 33835, Mode For categorical feature we can select to fill in the missing values with the most common value mode as illustrated below 39244, Drop irrelevant categories 8888, One Hot Encoding 3911, LotFrontage we can fill in missing values by the median LotFrontage of the neighborhood 39032, Train 38491, Submission 9171, TotalBsmtSF 17719, To limit the number of categories in Fam membs features it be divided into 4 categories as following 21077, Target varaiable 8025, Let replace missing value with a variable X which means it s Unknown 5519, XGB Classifier 13166, start loading the required librarys that we use in this kernel 640, so we have 18 different titles but many of them only apply to a handful of people 26736, Plotting sales over the months 30644, Logistic Regression 3847, option4 replace age with title of name 40469, Output new files 2987, Decision Tree Regression 7555, Bagging And Pasting 117, As I promised before we are going to use Random forest regressor in this section to predict the missing age values 19722, Store CA 1 22363, In this case three duplicated values are in Public LB subset 39771, As I suspected questions are indeed sorted by ascending question id in our train dataset 2225, Building Characteristics 38556, Imputing missing values 31112, there is no obvious seasonal periodic characteristics 26440, To get an overview of the model structure we can plot the specifications and the model architecture 21379, NB 31774, Prediction 37629, Here we display some images and their transformations 14413, Scaling the Data 8471, Feature Selection into the Pipeline 40702, Training and Evaluating Our Model 17252, Voting Hard Soft 33149, Predict 35657, Lets isolate both the numerical and categorical columns since we be applying different visualization techniques on them 1178, No incongruencies here either 4230, Data Load 25179, After we find TF IDF scores we convert each question to a weighted average of word2vec vectors by these scores 6265, This confirms that Southampton had the most number of 3rd class passengers but also had the most number of first class passengers 18295, Checking our synonyms 3798, For the categorical features without what NA means fill the NA with the mode 37640, Adding variables from bedrooms 42251, Import machine learning modules 7699, define some helper functions which would be used repeatedly 31402, Train Model Submit 2181, Box Cox transformations aim to normalize variables 42096, Plotting labels and checking their frequency 38688, Sex 9743, Age 19457, The final layer of 10 neurons in fully connected to the previous 512 node layer 2211, Light GBM 6850, Ordinal Features 30339, This is a routine to evaluate the losses using diferent learning rates 19214, Sorting 27052, Preprocessing DIOCOM files 10783, Gridsearch and Crossvalidation 8793, To explore the data we start with 20043, Load Data 1088, Statistical summaries and visualisations 3857, Drop and Reordered columns 21407, Training 16902, New Feature NameLenBin 27318, Creating a simple model without any hyperparameter tuning 28107, Find correlation and combine the highly co related columns 11850, Final Filling of Missing Data tying up loose ends 18249, Dates Transactions 20834, It is common when working with time series data to extract data that explains relationships across rows as opposed to columns e 41273, Text Annotate Patch 16026, Around 74 female survived but male just 18 22438, Counts Plot 24846, Still need to complete part of the data for dates past the 25th of March as the enriched dataset didn t go that far 34282, Cnt Along Time 13059, Importing tools data and cleaning up the data 41962, Predictions 14442, Create New Column AgeBand 25004, Item Item Category Data 24150, we calculate the mean hour for each product 12148, Creating the submission file 3 22103, Visualiza some Test Images and their Predicted Labels 12107, XGBoost 20222, Submission 14909, People from Pclass 1 and Sex female are more likely to from Embarked S or C 2122, The parameter space I want to explore searching for the best configuration is 38416, Don t forget to change the model prediction from a class probability array to class value 12631, Feature Distributions 37667, Callbacks 4973, IsChild 42029, For example PC17599 PC and 113803 113083 18067, Saving the Models 11103, View sample data 11140, 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 19546, Stemming and Lemmatization 20715, LotShape column 35461, Visualize the skin cancer at Upper extremity 29903, Compile network 9353, Wrangle prepare cleanse the titanic data manually 15136, Engineering 22466, Tree map 10319, Make individual model predictions 33497, Italy 30603, We can remove these columns from both the training and the testing datasets 3832, box whisker plot using plot method in pandas 4403, Bulding the Random Forest Model 41207, len prediction len GT 26337, The best score is when we use MNB on the bag of words vectors 21364, Using polarity for pretraining TRANSFER LEARNING 39073, Feature Importance 40689, now most importantly split the date and time as the time of day is expected to effect the no of bikes for eg at office hours like early mornning or evening one would expect a greater demand of rental bikes 4436, Prediction 27183, Creat Submission file 28707, Submission 21640, 3 ways of renaming columns names 38273, here we are actually padding that means if the sentence is not long enough we are just filling it with zeros 36409, look at filling in some of the tax related variables 31528, Explotory Data Analysis EDA 21786, Var columns 6531, Visualize model scores 33466, StateHoliday 31640, PdDistrict 8345, we submit our prediction 38563, K Nearest Neighbors Regressor 450, Getting dummy categorical features 3672, Visually comparing data to sale prices 17687, WE WILL USE THE MEDIAN VALUE 12944, Categorical Encoding 31674, Understanding how to process data for our aim 3869, what happened 38301, Lets now prepare and clean our training dataset 7831, Rolling window estimates 29361, Fare Feature 35420, Plotting an image from each class to get insight on image data 34865, I use the following function to track our training vocabulary which goes through all our text and counts the occurance of the contained words 38092, FITTING THE MODEL 27575, ps ind 03 x ps ind 161718 3881, We are at the final step of applying Regressors to predict the SalePrice with our pipeline 21647, Read and write to a compressed file csv zip 33661, Put submission to csv file 7748, There are some categorical features which their categories are actually ordinal so it can be a good idea to convert them to numerical features 33506, Albania 15852, Ticket Family Survival Rate 32483, Re run model using all training data for optimal mixing parameter 26220, Horizontal Flip 18264, Predictions class distribution 24057, We ll build a CatBoost model and find best features with SHAP Values 22066, Which words in our data are NOT in a vocab 3760, Linear Regression 2465, Forward Selection 40281, define helper function for image augmentation using albumentations library 19011, Experiment 1 26877, Create Submission File for approach 1 39998, At this stage I compare two data set according to the age 15999, Creating the Submission File 41657, Bivariate analysis of numerical features against target 7747, Id feature can be deleted as it doesn t give us any information and it s not needed 33243, the training cycle is repeated lr find freeze except last layer 38621, Splitting Up Train and Validation Set 8886, Skewness Check in all the columns 18501, Simple enough 31352, The report can also be exported into an interactive HTML file with the following code 10855, Filling certain categorical columns about which we have an intuition using the forward fill method 32366, A General Look With Sunburst Chart 18035, Men and Boy 16827, Feature Selection Using RFE 29855, We need to perform tokenization the processing of segmenting text into sentences of words In the process we throw away punctuation and extra symbols too The benefit of tokenization is that it gets the text into a format that is easier to convert to raw numbers which can actually be used for processing 26467, In this section we try to implement a CNN architecture from scratch using the Keras library 33202, Evaluate Model on Test Data 2049, LogisiticRegression Model 27016, Training 6297, Model selection 18490, Since the competition variables CompetitionOpenSinceYear and CompeitionOpenSinceMonth have the same underlying meaning merging them into one variable that we call CompetitionOpenSince makes easier for the algorithm to understand the pattern and creates less branches and thus complex trees 55, AdaBoost 41301, How many categorical predictors are there 14154, let s take a look at the Cabin and Ticket features 10640, Rarely Occuring Title Respective Gender 12083, Correlation study 43378, the label holds the true digit and the other columns all 784 pixel of an image with 28 times 28 pixels 9213, Family Survival 19145, Model 4 with AdamOptimizer 11907, Embarked 28482, The standard deviation and the maximum value have dropped by quite a large margin 14539, We are given the train and test data 24351, Set the optimizer and annealer 15668, Logistic Regression 2766, Feature selection 11542, As a first step we start by importing all the libraries that we shall use in our subsequent analysis 1931, Ground Living Area w 29602, Training The Model 12187, Submission file 21121, Define variables categories 18188, Lets clean up product names a bit we have 1000 unique names once we cleaned the weights but there is much more work to be done 7373, Preparing Kaggle dataset for merge 3240, Importing and Understanding the Data Set 12401, Converting ordered categorical fields to numbers 6909, And the next 3 plots are just cool kdeplots for Gender and Survival Rate 31097, Data cleaning for test data done 17273, Gaussian Naive Bayes 17890, Lets visualize the new variable Title 26263, Processing of the test file 6897, Most of the passengers are between 15 and 40 years old and many infants had survived 40725, Further Traning 795, You can easily compare features and their relationship with the class by grouping them and calculating some basic statistics for each group 15152, Age vs Survival 9252, A Few ML Models 31046, Caps Words 11677, It looks like those who survived either paid quite a bit for their ticket or they were young 21061, ROC Curve for healthy vs sick 34611, PCA 12669, Visualization 27553, Display interactive filter based on click over dependent chart 11130, Safe to drop the Id column now as we are finished deleting points otherwise our Train ID list be smaller than y train 28516, GarageCars 22370, Feature Selection 40825, t SNE using Scikit Learn 35799, Find best cutoff and adjustment at low end 19826, Rare Label encoding 6010, Masukkan ke pipa Preprocessor 24499, Generating the model itself using the defined class 30094, To enable CV 2748, Using mode returns a series so to avoid errors we should use 15524, Combine train and test dataset 32112, How to import a dataset with numbers and texts keeping the text intact in python numpy 32320, Check Accuracy 28336, Analysis Based on FAMILY STATUS 41934, Generator Network 25287, 7 accuracy not bad at all 28811, Create Our Model 20036, The next step is to split the data into training and testing parts since we would like to test our accuracy of our model at the end 27173, Splitting the data in train and test datasets for model prediction 13468, Exploration of Embarked Port 4301, Inference 3484, Model 3 Random Forest 25846, Hashtags Cloud 20383, explore corpus and discover the difference between raw and clean text data 3371, In order to actually move my local files to GCS I ve copied over a few helper functions from another helpful tutorial on moving Kaggle data to GCS 1180, Looking at the min and max of each variable there are some errors in the data 6696, Lets take some examples 16105, we map Age according to AgeBand 34919, Count exclamatory in tweets 11995, make predictions on test data for linear reg 27452, Remove Punctuation 4158, Count and Frequency Encoding 18351, ENCODING 40730, Lets understand the intermediate layers of the model 8545, LotFrontage 37060, Ticket variable contains alphanumeric only numbers and character type variables 29915, The random search slope is basically zero 15203, Neural Network 16406, Male is Less likely to survive than female 33697, Monthly Series font 18892, Modeling 32225, First we need to define the label and feature 4865, SalePrice is skewed and it needs to be normally distributed 3806, Gradient Boost Regressor 6398, Data is skewed and dense at bottom checking for skewness and kurtosis 8546, Garage Features 38956, And then unpack the bbox coordinates into seperate columns x y w h 11017, we are gonna write a code which iterate over Sex and Pclass and fill the null matrix 12460, Skewness in other variables 5549, Build Keras Model 27979, Binary Classification 24587, Predict 25225, Modeling 22707, Prediction on Test Set 34966, Data Wrangling Feature Engineering 8945, Fixing Masonry 8353, Survival rate regarding the family members 276, Age 10848, Box Cox Transformation on Skewed Features 30576, Correlation Function 14978, Filling Cabin missing values of training set 28708, Randomize the samples from TRAIN DIR and TEST DIR 7073, It s a passenger from Pclass 3 so we ll fill the missing value with the median fare of Pclass 3 23962, Correlation Analysis 25765, Pclass 10482, Imports 26381, start by defining some parameters 39752, And again checking poly scaled features making sure to scale AFTER you add polynomial features 35882, Reshaping 28539, is time for modeling 30424, Sample sentiment prediction 41051, Customer Order Count 35719, looks like we have 2 MasVnrArea and BsmtFinSF1 so use those for this test 13968, SibSp Siblings Spouse 17881, Spliting the train data 8082, Defining model scoring function 16383, Combining Pclass and Embarked 41732, Setting up validation and training dataset 9418, The model Cross validation 8918, The function plot missing identifies the percentage of missing rows for every feature in our dataset 2927, ExtraTrees 41930, let s create a dataloader to load the images in batches 23842, Taking X10 and X29 14463, back to Evaluate the Model model eval 7980, Its hard to select from them by eye 36491, Attention model in Keras 6802, Imports and useful functions 13342, Another piece of information is the ticket number 1726, Correlation Heatmap 7937, XGBoost Regressor 6950, Embarked 1893, Validation Data Set 32380, Correlations Between Features 26794, Visualize some examples from the dataset 13029, Fare 42979, now construct a few features like 16959, We have a clear fare distribution between 0 and 150 16087, Below we just find out how many males and females are there in each Pclass 11196, xgb reference 41581, Using a Learning Rate Annealer the Summary 803, Imports 38022, First let us define these functions which do the jobs 34342, Feature Selection and Engineering 3179, Sklearn Models 1388, Validation 6098, Transforming and Engineering Features 6753, Checking Skewness for feature LotArea 42787, Target Distribution 18607, ConvLearner 725, As aforementioned if we want to look at more traditional regression techniques we need to address the skewness that exists in our data 29628, Dataset after transformation 38017, Another handy feature is analyzing individual predictions 15258, One Record is missing for Fare Feature in test dataset 1629, Importing my functions 18222, Evaluation Functions 15713, Create one feature for Family Members on board of Titanic 9172, This relationship isn t looks almost linear 15797, There is much difference for 1st and 2nd Embarkation for 1st and 3rd Pclass in terms of fare for males and females while the 2nd class fare is similar in all the Embarkations 15209, calculate average survivability for each left categorical fields 11623, Split train and test dataset 6383, Histograms are used to visualize interval and ratio data 6520, Numeric Variables 26915, It is often considered as if there is more than 15 of missing data then the feature should be deleted 28468, Remaining columns 12499, Train Untuned XGBoost 35921, View an example of features 35675, Based on the current feature we have the first additional featuire we can add would be TotalLot which sums up both the LotFrontage and LotArea to identify the total area of land available as lot We can also calculate the total number of surface area of the house TotalSF by adding the area from basement and 2nd floor TotalBath can also be used to tell us in total how many bathrooms are there in the house We can also add all the different types of porches around the house and generalise into a total porch area TotalPorch 7758, Another pipeline for categorical features which first use an imputer to replace missing values of features which the most frequent value and then applies a label binarizer 976, tune it 4912, Analyzing the Categorical Predictors 38982, Keep only relevant numerical features and normalize 19158, Undersampling 2420, It appears that the target SalePrice is very skewed and a transformation like a logarithm would make it more normally distributed 19914, Feature Importance 32120, How to find the position of missing values in numpy array 29531, displaying the matrix of a single image defines the first instance of the data 15546, dropping Cabin column 2768, Interesting insights 10254, Go to Contents Menu 2579, Libraries and Data 6606, XGBOOSt 26018, Dealing with outliers requires knowledge about the outlier the dataset and possibly domain knowledge 535, Feature Engineering 24825, change grey value from int to float 39145, Data Wrangling 14475, 18 of male survived and 74 percent female survived 27554, Display interactive highlighter of time series 36835, Define calculations we need from the Neural Network 17830, also plot the classification report for the validation set 12421, After training our model we have to think about our input 42990, Word2Vec 10780, Object Columns 24101, Finding the columns contains nan value 37117, We get a correlation coefficient of 0 8138, Imputing Real Null Values 203, RANSAC Regressor 5962, Age and Survived 5337, Diplay series with high low open and close points with custom text 1116, 20 of entries for passenger age are missing 14409, We dont need this feature to predict the survival of a pasenger 41812, Inferance 6912, Check for missing data 15365, Handling Embarked Feature 887, Gaussian Naive Bayes 41077, Encoding the data and train test split 1932, SalePrice per square foot 41279, MEME xkcd theme 24996, Generating final training validation and test sets 9277, Lasso Regr Model 14513, Observations 37181, Submission 28427, Shop info 15752, Clean Data 4093, Last but not the least dummy variables 39691, Spelling Correction 7464, Making predictions and measuring accuracy 9032, In one hot encoding we made values of categorical nominal features their own row 8074, Zoning is also interesting 36752, Submission File Creation 12784, Training the model 14226, Binning 26341, Modelling 3489, Get the in sample and estimated out of sample accuracies 11041, Create the pipeline 29881, With KFold 11826, look at the data after dropping variables 43358, X and y 38580, There are more missing values in the keyword and location so we can drop it 13987, Age is not correlated with Sex and Fare 4990, Learning Logistic Regression 41175, update i changed maxpooling layer and dropouts with multisample dropouts 24523, How about employee index 32968, Embarked 8375, XGBoost 1761, Passenger ID 4316, Fare 34608, using the XGB models 38203, Prepare Data 6386, calculate and plot Pearson correlation coefficient using numpy for columns in the dataset and plot them using seaborn heatmap 19098, Building Machine Learning Models Train Data 13126, Visual of empty values 13702, We now use one hot encoding on the deck column 32128, How to create a new column from existing columns of a numpy array 19545, Removal of Stopwords 9425, Scatter Plot 28783, Conclusion Of EDA 6290, Support Vector Machines 27036, The number of unique patients is less than the total number of patients 13529, Encoding categorical features 34531, To test whether this function works as intended we can compare the most recent variable of CREDIT TYPE ordered by two different dates 36853, plot history loss and acc 10778, good enough for me I ll wrap this up and make the predictions out of it 6562, Parch 43117, handle missing values in X test 23585, More insights 32533, Train the Model 27400, Load images into the data generator 6783, Feature Sex 7030, Garage Quality 37900, XG Boost Regressor 3783, XGBoost 40316, Get only those lemmas with 2 merged candidates 5719, Getting dummy categorical features 11387, The passenger class is somewhat evenly distributed in terms of survivors but of those that perished odds were they were in the 3rd class 31407, Lets take a look of our data notice that the grey img now is turned to RGB with size 4532, Titanic 1997 film 29Historical characters 34628, Wikipedia Definition 14101, center Histogram center 16591, Checking best features 9504, Librarires using for Machine Learning Algorithm 32245, let s split out training and testing data 20351, we unfreeze the pretrained layers and look for a learning rate slice to apply In Lesson Call lr find again Look to just before it shoots upe and go back x which is e and that s what I do for the first half of my slice and for the second half of my slice I normally use whatever learning rate I used for the first part divided by or 6558, Age 1328, Analyze by visualizing data 31122, normalize and impute missing values 37992, Predict on test 4123, Machine Learning 8658, Instructions 14985, Machine Learning with different algorithms 13228, Deep Learning Model 33039, That didn t change it much 15551, Training data 10280, Random forest full dataset 6235, Stacking of base models 13523, If we want to it s also possible to check the feature importances of the best model in case they re easy to understand and explain 7721, Checking for NaN values in Data 32211, Add lag values for item cnt month for month shop item 42307, Probability Dataframe 25213, find the percentage of missing values for the features 38720, we finished training the GAN 28997, Dealing with Outliers 248, Library and Data 16512, Logistic Regression 15879, Leaf size 30662, For somebody storms and open wounds are the real disasters 7866, To start the exploration it is possible to group passanger by Sex and Class these groups could give insights if higher class have better chance of survive or woman have better chance than men for example 14537, Random Forest Classifier 15882, Output Final Predictions 4769, Replacing the fare column in the test dataset with the median value of the column 4109, Transform the target variable if skewness is greater than 1 or smaller than 1 14116, As expected the survival rates are higher if they are with family 37807, Polynomial Regression 18453, strong Natural Language Processing strong font div 42257, Make predictions for submission 13410, Precision 16271, Execute 35576, Looking at the same data a different way we can plot a rolling 7 day total demand count by store 7594, Rotating the 3d view reveals that 32200, Clean item names 5537, Final Missing Data 9385, BsmtFinSF1 Type 1 finished square feet 19346, Prediction 41003, Classifier block 31708, Stacking Blending 11485, Electrical 17486, Stacking Ensemble 3194, Full imputation 13182, First let s take a look into Age distribution 38951, For this particular problem we can just use one time period of 48 days 42033, Narrowing down filtering small categories using threshold 27467, Data Cleaning 18964, Display the variability of data and used on graphs to indicate the error 4311, Survived or died 20444, previous applications 32352, Loading pre trained word vectors 459, Mean of all model s prediction 24271, k Nearest Neighbors algorithm k NN 34089, Apparently there are some price outliers we need to remove for visualization 28914, We ll back this up as well 7458, Let s take a look at the Age column 19932, SibSP 21738, When the model is finished process the test data and make your submission 10900, Imports 31056, Latitude Longitude 38828, Define model and optimizer 29992, Splitting the data 4511, Considering the number of Outliers and missing values also we are discarding these variables 4237, We can now evaluate how our model performed using Random Search 21778, Missing data 39774, Splitting into train and test with sklearn model selection train test split 444, there any many features that are numerical but categorical 40648, encoding numerical features 14271, Cross Validation 34618, Model parameters initialisation 39689, Remove punctuations 31741, Hue Saturation Brightness 1242, Submission 26044, Defining the Model 12028, one more thing we may experiment as we know Xgboost LGBM Adaboost and Gradient boosting fits well so let s try to combine only these four using stacking 42461, NOTE I take a sample of the train data to speed up the process 8721, Summary 5129, Model Ensembling 27650, Set data features and labels 39436, Remove Object type of feature from train and test datasets 12598, Data 38180, Finalize Model for Deployment 4267, SaleType 28826, Extract the dates of state and school holidays and format them in a DataFrame adhering to the need of the prophet 34912, Count users by 29950, The feature importances look to be relatively stable across hyperparameter values 23333, Ensemble Performance 24865, Before moving on lets split train df into X and y to prepare the data for training 6069, MiscVal I would say drop it 5455, Stack them into a single array 26289, Implement the update rule 4975, Family Size 39437, Visualization 7153, Loading Required Librarys and datasets 13690, Age 18459, Embeddings I ll move on to loading and using the embeddings tools 7652, transform skewed features 17378, We model our testing data the following the same way we did with the training data The following steps 10047, Reciever Operating Characteristics 40282, Define dataloader for tpu 21425, MasVnrArea 26502, We ll get back to convolutions and pooling in more detail below 14928, Naive Bayes 11021, Completing missed values in Embarked feature 5966, Child feature 25342, Parameter and Model Selection 11176, our column count went from 216 to n component value 7216, Alley Null Values 20738, GrLivArea Column 25038, Looks like customers order once in every week or once in a month 27585, that we have an idea of what new features to construct and how they might be useful let s add the rest of them and visualize them 11780, Missing Value Treatment 40254, 1st Floor Surface Area 11153, Look for other correlations maybe all the basement olumns correlate like BsmtFullBath and BsmtFinSF1 and Fin vs Unf have negative correlation 8922, In our EDA section we found the relationship between Neighborhood and LotFrontage 26992, Apply lowerization necessary if using paragram 17807, And let s plot the Class Age grouped by the survived and not survived 37821, Remove URLs http tags 13288, We tuning the hyperparameters of the XGBClassifier model using the HyperOpt and 10 fold crossvalidation 21907, Examining highly correlated variables 34650, Borrowed from 25875, Word clouds of each class 37881, Evaluation 84, apply function in each unknown cabins 30985, Distribution of Search Values 28002, Buiding The Model Structure 35233, Sentiment variable is the theme of our data 6106, Don t forget to save the indexes of primary datasets 11545, The target variable is positively skewed we can perform a log transformation to render the target distribution more gaussian like 40101, Make submission 36291, Additionally you can view the y intercept and coefficients 36862, Perceptron 17958, NN 22935, I create an estimated age feature 5495, K Fold Techniques 42844, Italy 8113, Splitting Training Testing Data 26581, And what a good boy it is loop over dog images to view some wrong predictions 9520, Logistic Regression 19897, Bottom 10 Sales by Shop 38854, Prediction 40785, nn model with L regularization The general methodology to build a Neural Network is to 13443, SibSp don t have big effect on numbers of survived people 27288, Plot SEIR model and predict 26299, Predicting and saving the output 3299, MiscFeature delete According to the file you can use None to fill in missing values I also explored the relationship between MiscFeature and its corresponding MiscVal and GarageType tried some filling methods and even determined that the example in the test set 1089 should be filled with Gar2 but eventually deleting the feature works better for my model 10554, Reverse The Labels 32629, well the naive ba on TFIDF features scores much better than logistic regression model 25796, Some managers have entries only in one of the two datasets 6982, Discrete Variables 28721, Looking the Total Amount sold by the Stores 11419, Use Case 11 Funnel Chart 17005, There are a lot of missing values in Cabin column 527, Passengers embarked in S had the lowest survival rate those embarked in C the highest 41114, Target Variable Distribution Join Fips by Bokeh 2937, Make the Distribution Normal 18486, get Days from Date and delete Date since we already have its Year and Month 41741, Looks like they were moved but only a tiny bit 33546, Current best value is 14793 18075, Plot the images with many spikes 20843, 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 1980, apply that function 42145, Encoder 16665, Creating New Features 4749, YearBuilt YearRemodAdd GarageYrBlt sown us when year is increasing price is also increasing 28863, Pytorch Tensors 1691, Distribution plots for list of numerical features DistplotsforallNumricals 32710, Defining optimizer 28221, Model Tuning 14241, Fare Features 33887, installments payments loading converting to numeric dropping 14648, Brilliant time to test our first model 22272, Cabin 19909, Year 15837, Fare 26455, RF Modelling 32941, ik word P Xi 124 y Likelihood of each word conditional on message class 13019, Analyzing the data 42992, Modeling 32930, Predicting with images from test dataset 21920, RMSE on test set using all features 12906 42224, Data Augmentation 445, Converting some numerical variables that are really categorical type 18209, Submit to Kaggle font 16654, When I googled that names I learned that they boarded the Titanic in from Southampton 16015, Normal distribution outlier not detected 19651, Benchmark predict gender age group from phone brand 40625, we are ready to build and train out pytorch models 9960, I create this new data frame were i select the columns SalePrice and MoSold 34298, It is a 222x222 feature map with 32 channels 11424, Find 22132, The Hyperparameters that can be used tuned in CatBoost model are 28927, All 22625, Visualizing Model Performance 3772, Parch 34279, sidetrack Rent cost in NYC 6157, Create a meta regressor 28407, Compile It 33323, Define callbacks and learning rate 37538, ANN KERAS MODEL 23111, Findings Nearly 58 passengers had title Mr male of course followed almost 20 passengers had titles Miss unmarried women hence usually younger than Mrs Just over 15 passengers were married women Mrs 34939, Predict submit 15395, have a look at missing values across the board 41461, Feature Embarked 23438, Making our submission 32117, How to compute the softmax score 19257, Rearrange dataset so we can apply shift methods 20815, SUBMISSION 4828, LotFrontage Linear feet of street connected to property 5840, We use log transform to make them normally distributed 35426, Compiling and fitting the data in the model 30838, Lets look for the top crimes of San Fransisco 42219, The next step is to feed the last output tensor into a densely connected classifier network a stack of Dense layers 29114, train fit one cycle for 5 cycles to get an idea of how accurate the model is 36585, Use more training data 37001, Number and ratio of orders from the three datasets 31302, PCA decomposition 8767, Survival by number of sibling or spouse 28459, Analyzing columns of type float 7360, Leave first 40 features 39736, SibSp and Parch 15631, Visualizing age data 17794, check the correlation between family size and survival rate 38633, write a function to display outputs in defined size and layer 5002, Numerical Features 32015, There is one Ms so we assign Ms to Miss category and NaN to all remaining rows In below code block negates the rule 19565, Preparing the data 41533, Dimensionality reduction 38069, Bigrams Analysis 7514, Optional code cells 37361, SibSp Parch vs Survived 13568, Sibsp feature 10803, presume that Fare and Class are related to Port 3616, Remove Outliers 36559, Setup 22907, Encoding and Vectorizers 8748, Validation Function 17346, Extra Trees 11326, Fare Imputation 17364, preparing submissions 6028, Auto Detect Outliers 10264, Distribution of target variable 5189, In machine learning Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes theorem with strong naive independence assumptions between the features Naive Bayes classifiers are highly scalable requiring a number of parameters linear in the number of variables features in a learning problem Reference Wikipedia 32654, Whenever possible additional features related to key aspects of the problem under analysis may be created to reinforce the weight of such aspects in the regression 22973, Display Influential Subtext 15139, Checking Survival rate 26500, For this problem we use zero padded convolutions so that the output is the same size as the input Stride step in this case is equal to 14120, Correlation 41405, Contact information 2542, For this part I repeat the same functions that you can find previously by adding a categorical features 7941, Submission 19401, We are going to vectorize the text along with increasing the readablity of the text by removing the punctuations and countwords 37233, Train 21785, Inference 30583, we can handle the one numeric column 40984, Aggregate by sum and mean 24826, format 12222, The coefficients are a bit different but we did not solved much 9410, Conclusion 7589, Boxplot SalePrice vs OverallQual 36275, Pclass and age as they had max relation in the entire set we are going to replace missing age values with median age calculated per class 37989, flowers transfer learning 3 27419, More num iterations 29157, PoolQC Fill with None 6555, Survived People with more than two siblings or spouse are likely survived 17690, INITIALS AGE 32524, Model Refining Model 10396, In this project we use python package called vecstack that helps us stack our models which we have imported earlier It s actually very easy to use you can have a look at the documentation for more information 13621, pclass It be relevant remmember in the movie how the first class passengers were being taken to the boat first Hell they took the dog too 10969, Rescaling 31516, Selecting a feature set based on ExtraTreesClassifier 12809, Missing Values 11665, XGBoost 24854, We can quickly check the quality of the predictions 28944, We can check for the best model by comparision in just one line of code 17820, We predict the validation set 6518, Plot categorial features with target variable 4697, Normalisation 18540, Some correlations coefficients are changed and other are not 26862, Here I have defined two filters 7600, define data for regression models 7466, Making Predictions on Test data 9756, HasCabin 27401, Plot some sample images using the data generator 17722, The values differ from what is expected as there are people who are in Pclass 1 but paid low to no fare 35075, Complexity graph of Solution 6 7798, Create and Store Models in Variable 7246, Explaining Instance 3 2 3 3 30960, Domain 3840, fill Embarked column values 29164, Utilities Drop Feature 31584, let s check how the target classes are distributd among the IND continuous features 41789, Digits prediction 32064, Here n components decide the number of factors in the transformed data 37113, Embedding the glove vectors takes 3 mins on average 27344, item price 6622, Ridge Regression 34404, Training 11263, Visualizations 12453, This means that null values were the once I ve just replaced for the mode 36717, Pytorch Dataset Class 38909, Adversarial Validation 2, LotArea is a continuous feature so it is best to use panda s qcut method to divide it into 10 parts 13297, In contrast to majority voting hard voting soft voting returns the class label as argmax of the sum of predicted probabilities 40932, Iterative Folds Training 21362, Training on just the target label BASELINE 2290, ROC AUC Score 31115, Feature selection by correlation 19886, Lag Features 2406, Places Where NaN Means Something 9297, Predicting Survival based on Random forest model span 41486, Random Forest Predicting 25359, Most frequent words and bigrams 39000, Split the data into 60 as train data 20 as dev set and the rest 20 as test set Instead sklearn model selection train test split can be used 9363, Fare feature to ordinal values based on the FareBand 2693, look at the missing valeus in our data 16470, We plot feature importances of 4 classifiers excluding LDA and MLP 37669, Model training 33270, T1 is the same period as the one found in overall histogram 33230, Xception Nets 40188, Metric analysis 25446, Training dataset 15071, Gender 20736, CentralAir column 21074, To solve those documents where no word is present due to exception I have tried to use a Trick by taking the help of wordcloud of some other kernel writer 35368, Initialize Dataset 26369, Fitting the network 41615, that we have all of our data in the right format for all three modes of analysis lets look at the first one 655, For a final overview before the modelling stage we have another look at the correlation matrix between all old and new features 36930, Cabin 40317, WordCloud visualization 3802, Define a cross validation strategy 25381, investigate for errors 16594, Initializing Model and Training the the Model 22639, Model 4 Lasso 24771, Stacking algorithms 5347, Diplay number with guage 38888, Dataset 18510, This gives us some idea with the sort of images we re dealing with 28945, Lets use categorical boost as it performed best 38469, Doing the same for macro features 13409, Classification Error 31334, I choose to fill the missing cabin columns with 0 instead of drop it becuase cabin may be associated with passenger class We have a look at a correlation matrix that includes categorical columns once we have used One Hot Encoding 29776, Predict 40275, Zoning Classifcation vs Sale Price 15580, Evaluating classifiers 11430, Would you remember KitchenAbvGr just be dense on 1 20710, Third column 34681, Taking a look at what s happening inside the top categories 19362, Within bivariate analysis using scatter plots 32944, Plot for duplicates 40953, Making the New Training Testing Sets 17747, Imputing Missing Age Values 40082, Fill in missing values 34438, Target correction 15677, Predictions based on tuned model 43271, Fazendo as previs es nos dados de valida o 11807, Handle missing values and categorical features 9259, Predicting formatting and prep the submission file 20094, With no parameter tuning decreasing trend and yearly peak are correctly predicted 22395, In addition to NA there are people with very small and very high ages 40936, To tune the hyper parameter for TabNet we need to make a small wrapper The fact is in TabNet PyTorch ai tabnet implementation TabNetClassifier does not have a get params method for hyperparameter estimation yet 14917, Convert Title column into category variables 130, we have our confusion matrix 18252, Train test split 4145, CCA on Titanic dataset 14354, Male and female distribution in Survived Passengers 6167, Name 7610, Loop over Pipelines Linear 4413, Check that missing values are no more 30828, Getting the Libraries 6073, Conclusions 43019, Modelling 42952, Parch Feature 35888, Separate submission data and reconstruct id columns 30638, Dealing with missing age 13097, Survival among various categories 19456, Second Hidden Layer 13374, Here 0 stands for not survived and 1 stands for survived 1336, we can safely drop the Name feature from training and testing datasets 22481, Cluster plot 1677, Peek Data Setting the context view 10547, XGB Regressor 23662, Rolling Average Sales vs Time per dept 12822, Lets clear our vision by another graph 41469, Leaving Embarked as integers implies ordering in the values which does not exist 35509, Before the feature engineering part train and test data have been merged 1815, Modeling the Data 9510, Data Analysis 33884, Bureau balance loading converting to numeric dropping 19388, Finish data preprocessing 16634, No of values in each category 21217, we tranformed the input vectors to matrices to get images like this 20945, Data visualization 13463, Exploration of Age 25162, Plotting Questions based on there frequency 13771, For men it s better not to be alone whereas women have higher survival probability by not having family on the boat Survival probability increases for men when they have a large familly In general having too large familly e 5 members reduces the chances of survival For being a child or not its the same constat as for being alone yes for man no for female 21123, look at missing values per variable starting from numeric features as they usually play decisive role in modeling 39676, Check the shape of the image we chose to paint and store that shape in a variable 20940, Evaluation 14671, Random Forest 13730, Combining SibSp and Parch to Fam 11828, Log Transform on SalePrice 24593, TESTING DATA CLASSIFICATION 13526, Import libraries 21134, Indeed our guide informs us that 34415, Number of words in a tweet 11845, Porch 29548, Punctuation is almost the same 38561, Model Functions 14895, Obviously survival rate of female is much higher than male 13210, finaly we can do the plot without problems 1935, Heating and AC arrangements 34179, CNN 30253, Run cross validation to find the most appropriate nround value 15508, Only one missing value 25403, ACTIVATION FUNCTIONS 9600, Group by 40452, BldgType 27775, Inspect your predictions and actual values from validation data 10770, Ticket grouping 5030, Interesting 22211, Time keeper 24832, General function 31523, Attempting to get the cross validation score 13464, The age distribution for survivors and deceased is actually very similar 39919, Data cleaning 36481, We save the private df in a CSV for further analysis it s up to you 12505, Tune eta 9116, Check to make sure there are no null values in our new feature neighborhoodTier 32931, Find the best epoch value 27631, 181108 where sold only once making up the vast majority of the data 27487, y hat consists of class probabilities 15193, Log Reg Xboost Svm and Others 15260, Data Visualization 6507, Check Missing Values 33863, Fitting Xgboost model 40129, Building a custom transformer to reshape the scaled images as required by the KerasClassifier 38979, training loop 29469, Univariate analysis of feature word share 930, Optimize Random Forest Regressor 19531, Creating Flat Maps 273, Library and Data 2195, Linear Regression 32697, The competition uses the following evaluation metric 41660, From section and the vizualizations in section two things are made evident 12302, Model Prediction 23556, Lets have a look at the year difference between the year of transaction and the year built 43284, Faz um fit usando todos os dados e n o apenas os dados que tinham sido selecionados como treino 5475, We now have a table of contributions Each row is a sample and every column is a field and the contribution to the predicted sale price 14073, Name Title 2221, Features from Outside 716, Again a slight increase 31272, Rolling Average Price vs Time CA 31928, We ll gain insight from the model evaluation on the test dataset 27984, Create a sample model to calculate which feature are more important 7274, Outliers 13163, CV Score 10590, XGBoost with parameters 12228, The model 16938, With this simple tree we have a way better model than the rich woman model 41060, Extract Features From Model 9018, unfortunately we are not done with the null features in the Garage Columns 1287, Dropping Unwanted Features 4 4 15871, we have all of training data again 6849, We can replace many titles with a more common name or classify them as Rare 29403, CHECK MATERIAL