fabiochiu/medium-articles huggingface dataset:
Recognizing Handwritten Digits with scikit-learn
Tags: Digit Recognition, Knn Algorithm, Scikit Learn, Machine Learning

The handwritten digit recognition is the ability of computers to recognize human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different flavors. The handwritten digit recognition is the solution to this problem which uses the image of a digit and recognizes the digit present in the image.

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently. Here we are going to analyze the digits data-set of the Sci-Kit learn library using Jupyter Notebook.

First we begin with importing the required libraries.

Now we load the Digits dataset into the notebook. After loading the data we can read lots of information about the datasets by calling the DESCR attribute.

The images of the handwritten digits are contained in a digits.images array. Each element of this array is an image that is represented by an 8x8 matrix of numerical values that correspond to a grayscale from white, with a value of 0, to black, with the value 15.

Let’s visually check the contents of this result using the matplotlib library.

The numerical values represented by images, i.e. the targets, are contained in the digit.targets array.

It is observed that the dataset is a training set consisting of 1,797 images.

This dataset contains 1,797 elements, and so we can consider the first 1,791 as a training set and will use the last six as a validation set. Here we can see in detail these six handwritten digits by using the matplotlib library:

Now we train the svc estimator that we defined earlier.

Now we have to test our estimator, making it interpret the six digits of the validation set and then compare them with the actual digits as follows:

Testing with a sample image

From the above output we can see that the targeted and predicted values are the same. Hence estimator is able to recognize the handwritten digits, interpreting correctly all six digits of the validation set.

Let’s predict the accuracy of our model using KNN classifier.

Conclusion:

Thus we can see how easy it is to import a dataset, build a model using Scikit-Learn, train the model, make predictions with it, and finding the accuracy of our prediction(which in our case is 98.33%).

Source Code: https://github.com/Saketh-Nandn/Data-Analytics.git

I am thankful to mentors at https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. Thank you www.suvenconsultants.com.
Recognizing Handwritten Digits Using scikit-learn Library
Tags: Scikit Learn

Machine Learning in today’s world has become a drastic driving force in the field of Artificial Intelligence (AI).

It is a process where a computer system is taught to observe and analyze a massive amount of automatically extracted data to make predictions using its perception

Today, my goal is to help you with learning a bit about image recognition with the help of an example.

Handwriting recognition (HWR), also known as Handwritten Text Recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices.

The name Python is pretty much synonymous with ML,AI and DA, so in this blog we will be using Python and particularly its library scikit-learn to recognize digits.

Scikit-learn, which we will be heavily using here, is a simple and efficient tool for predictive analytics, it is built on Scipy and Matplotlib.

Firstly, we’ll import the required libraries and Then we’ll import Datasets

The images of the handwritten digits are contained in a digits.images array. Each element of this array is an image that is represented by an 8x8 matrix of numerical values that correspond to a grayscale from white, with a value of 0, to black, with the value 15.

Now, Let’s train our model

Let’s do it for other number

This number is the probability for the digits in the test sample to be classified in the right category, meaning that we get 98.33% of the digits correct.

Conclusion

From this article, we can see how easy it is to import a dataset, build a model using Scikit-Learn, train the model, make predictions with it, and finding the accuracy of our prediction(which in our case is 95.11%). I hope this article helps you with your future endeavors!

Here’s the git hub link https://github.com/GPraneeth89/Performing-Analysis-of-Meteorological-Data.git

I am thankful to mentors at https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. Thank you www.suvenconsultants.com.
Alternative Python libraries for Data Science
By: Parul Pandey
Tags: Python, Machine Learning, Scikit Learn, NLP, Eda

1. Dabl(Data Analysis Baseline Library)

Dabl library has been created by Andreas Mueller, one of the core developers and maintainers of the scikit-learn machine learning library. The idea behind dabl is to make supervised machine learning more accessible to beginners and reduce boilerplate for common tasks. Dabl takes inspirations from scikit-learn and auto-sklearn. The library is being developed actively and hence isn’t recommended for production use. Refer to the official website for more info and examples.

Installation

# Installing the library

!pip install dabl



Usage

Dabl can be used for automated preprocessing of the dataset, quick EDA as well as initial model building as part of a typical machine learning pipeline. Let's demo some of the use cases of this library with the help of the titanic dataset. We’ll start by importing both the library as well as the dataset.

#import the basiclibraries

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt #importing dabl

import dabl #import the dataset

titanic_df = pd.read_csv('../input/titanic/train.csv')

titanic.info()

titanic.info() | Image by Author

Detecting Feature Types

As part of the preprocessing, dabl attempts to identify missing values, feature types, and erroneous data. If the detection of semantic types (continuous, categorical, ordinal, text, etc.) fails, the user can provide type_hints also.

titanic_df_clean = dabl.clean(titanic_df, verbose=1)

2. Exploratory Data analysis with dabl

dabl provides a high-level interface that summarizes several common high-level plots. All features are shown for low dimensional datasets; for high dimensional datasets, only the given task's most informative features are shown. Let’s see the process in action.

dabl.plot(titanic_df, target_col="Survived")

Target Distribution | Image by Author

Image by Author

Image by Author

Image by Author

All the above plots have been generated with a single line of code.

3. Initial Model Building with dabl

dabl can also quickly find a baseline model for our data. The SimpleClassifier implements the standard scikit-learn API of fit and prediction.

ec = dabl.SimpleClassifier(random_state=0).fit(titanic_df, target_col="Survived")
Recognizing Handwritten Digits Using Scikit-learn In Python
By: Urvashi Thakre
Tags: Machine Learning, Knn Algorithm, Jupyter Notebook, Digit Recognition, Scikit Learn

The handwritten digit recognition is the ability of computers to recognize human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different flavors. The handwritten digit recognition is the solution to this problem which uses the image of a digit and recognizes the digit present in the image.

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently. Here we are going to analyze the digits data-set of the Sci-Kit learn library using Jupyter Notebook.

First we begin with importing the required libraries.

Now we load the Digits dataset into the notebook. After loading the data we can read lots of information about the datasets by calling the DESCR attribute.

The images of the handwritten digits are contained in a digits.images array. Each element of this array is an image that is represented by an 8x8 matrix of numerical values that correspond to a grayscale from white, with a value of 0, to black, with the value 15.

Let’s visually check the contents of this result using the matplotlib library.

The numerical values represented by images, i.e. the targets, are contained in the digit.targets array.

It is observed that the dataset is a training set consisting of 1,797 images.

This dataset contains 1,797 elements, and so we can consider the first 1,791 as a training set and will use the last six as a validation set. Here we can see in detail these six handwritten digits by using the matplotlib library:

Now we train the svc estimator that we defined earlier.

Now we have to test our estimator, making it interpret the six digits of the validation set and then compare them with the actual digits as follows:

From the above output we can see that the targeted and predicted values are the same. Hence estimator is able to recognize the handwritten digits, interpreting correctly all six digits of the validation set.

Let’s predict the accuracy of our model using KNN classifier.

Conclusion:

Thus we can see how easy it is to import a dataset, build a model using Scikit-Learn, train the model, make predictions with it, and finding the accuracy of our prediction(which in our case is 98.33%).
Recognizing Handwritten Digits with Scikit-learn
By: Vishal Mishra
Tags: Pandas, Numpy, Data Analysis, Scikit Learn, Matplotlib

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents.

To address this issue in Python, the scikit-learn library provides a good example to better understand this technique, the issues involved, and the possibility of making predictions.The scikit-learn library enables you to approach this type of data analysis.

The scikit-learn library provides numerous datasets that are useful for testing many problems of data analysis and prediction of the results. Also in this case there is a dataset of images called Digits. This dataset consists of 1,797 images that are 8x8 pixels in size.

Our goal in this Internship project involves predicting a numeric value, and then reading and interpreting an image that uses a handwritten font.

So let’s get started step by step.

1. Import all the necessary libraries and load the dataset digits using the function.

2. Our data-set is stored in digits.

3. Let us train our SVM with the first 1790 images in out data-set. After that we will use the remaining Data-set as our test data and check the accuracy of our training machine.

4. lets now see the digits.

5. let’s see how our digits look.

The six digits of the validation set

6. Each dataset in the scikit-learn library has a field containing all the information.

7. Fit the model.

As we can see we have achieved 100% accuracy.

8. Let us now define a function that will find the accuracy of our SVM and train our model with varying data-set. We will start with 3 elements in our training data and work our way up to 1790 data and store the accuracy of our models in a dictionary.

9. Visualizing Dataset.

Accuracy vs size of training-set

10. let’s plot a Heatmap.

11. plot Data.

Conclusion.

From this article, we can see how easy it is to import a dataset, build a model using Scikit-Learn, train the model, make predictions with it, and finding the accuracy of our prediction(which in our case is 95.11%).

Above 95% of our models the achieved accuracy is 100% . Hence we can easily conclude that our model works for more than 95% of the time.

I am thankful to mentors at https://internship.suvenconsultants.com for providing awesome problem statements and giving many of us a Coding Internship Experience. Thank you www.suvenconsultants.com.
All in This Together? Trends in COVID-19 Fatality Across London Boroughs From Machine Learning
By: Conor Hamill
Tags: Machine Learning, Covid 19, Scikit Learn, Data Visualization, Institutional Racism

Keywords: COVID-19, London, city, data science, pandas, geopandas, scikit-learn, geospatial, maps, race, matplotlib, modeling, boroughs, numpy, selenium, python, machine learning, data visualization

From the end of 2019, the SARS-CoV-2 pandemic has flooded our newsfeeds and headlines with a vast array of numbers; we measure the local growth using the R number, compare the effectiveness of lockdown measures in different countries using cases per population number, and we see the deaths that the official COVID fatalities miss through the excess deaths. Another measure of the seriousness of the disease, and one which can highlight where adequate medical care is not being brought, is the ratio of the deaths due to COVID and the cases diagnosed in an area. This is the quantity I will be exploring in this project.

We know that there are many health factors that affect the COVID-19 mortality rate, including weight, age, smoking and underlying conditions like diabetes and asthma. One of the more shocking statistics to emerge, however, has been the strong positive correlation between COVID prevalence and non-white persons. An analysis from the CDC published in August showed that, compared to white individuals, hispanic or Latino persons are 2.8 times as likely to catch the virus, American Indian or Alaska native persons are 5.3 times as likely to be hospitalised, and Black or African American persons have a fatality rate 2.1 times as high. These statistics are stark indicators, that despite what recent advertisements like to say, for many people, we’re most certainly not in this together.

To examine this discrepancy in COVID-19 prevalence across different groups and attempt to somewhat visualise it, I decided to look at the cases and deaths of the disease across the boroughs of London. I knew the Office for National Statistics (ONS) would have more detailed versions of the information I was looking for to investigate the trends, and as a large, international city with many different groups in it, London would provide me with the range of data I was looking for to gain some understanding of what affects a person’s chances of surviving catching the coronavirus.
Recognizing Handwritten Digits Using Machine Learning
By: Sandeep S
Tags: Digit Recognition, Scikit Learn, Machine Learning, Internships

This article serves as a documentation of my second project — Recognizing Handwritten Digits for my internship program at Suven Consultants & Technology Pvt. Ltd.

The main objective of this project is to understand the classical problem of Recognizing Handwritten Digits by computers. This is a classic classification problem in machine learning that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently.

Included among the other applications that may come to mind is OCR (Optical Character Recognition) software. OCR software must read handwritten text, or pages of printed books, for general electronic documents in which each character is well defined.

But the problem of handwriting recognition goes farther back in time, more precisely to the early 20th Century (1920s), when Emanuel Goldberg (1881–1970) began his studies regarding this issue and suggested that a statistical approach would be an optimal choice.
Tired of making SK-Learn models on Kaggle? Models to Beautiful Web Apps in few minutes for FREE!
By: Aakash Gupta
Tags: Heroku, Streamlit, Web, Scikit Learn, Web Applications

Exporting the Model

There are many libraries/approaches that can help us in this

Pickle Approach Joblib Approach Manual Save and Restore to JSON approach

Each of the Approach has few advantages and few disadvantages. This tutorial will be showing the usage of Pickleshare. Pickleshare is the most commonly used libraries and works well most of the time. More details about each approach can be accessed using the link in the references section.

First of all, we need to train the model then test the model and calculate the metrics.

If satisfied with the results, Proceed!

Then, import pickleshare library and dump the model

Dumping the model

In kaggle, the output file is available to download in the output tab, simply right-click and download or after committing, scroll down to the bottom and download.

Downloading after Committing

This .pkl file contains our trained model that we will use in our web application.

PS: The data set is not needed anymore.
Keras Dataset Loading with helper functions
Tags: Dataset, Scikit Learn, Keras

Keras becomes a synonym for deep learning with computer visions nowadays. It has a nice built in API for building deep learning models such as Sequential and Parallel model. It provides a high level function to built, train and test deep neural network model such as CNN, RNN and LSTM.

However in this post, I will discuss another nice feature of Keras, helper function to load built in datasets that comes with Keras installation.

There are following dataset that can be load with load_data() functions.
Handwritten Digit Recognition in 10 minutes
By: Anmol Arora
Tags: Matplotlib, Data Science, Scikit Learn, Handwritten Digit

Using scikit-learn to built a handwritten digit recognition model.

Digit Recognition

What is Handwritten recognition ?

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently. Included among the other applications that may come to mind is OCR (Optical Character Recognition) software. OCR software must read handwritten text, or pages of printed books, for general electronic documents in which each character is well defined.

But the problem of handwriting recognition goes farther back in time, more precisely to the early 20th Century (1920s), when Emanuel Goldberg (1881–1970) began his studies regarding this issue and suggested that a statistical approach would be an optimal choice.

To address this issue in Python, the scikit-learn library provides a good example to better understand this technique, the issues involved, and the possibility of making predictions.

Model Building

Importing libraries and loading dataset.

In this model we will import the dataset from scikit_learn which consist of 1797 different images and their corresponding target variable.

libraries and dataset

2. Data Visualization.

Viewing first image

Plotting first image

3. Model designing.

We will use Support Vector Classifier for this model.

Support vector classifier

4. Training and testing

As our dataset is not so big , so we will train and predict 3 times for different splits of our dataset to get an average accuracy score.

> Training data = first 1791 images , Testing data= last 6 images.

1st Training and Testing

Accuracy score

1st prediction score

>Training data= Images from 6th to last , Testing data= first 6 images.

2nd Training and Testing

Accuracy score

2nd Prediction Score

>Training data= First 1785 images , testing data= Images from 1785 to 1790.

3rd Training and Testing

Accuracy score

3rd Prediction Score

Average accuracy =(100 + 83.33 +100)/3 = 94.44 %

Conclusion: Out of 18 our model predicts 17 correct digits for their respective images .

Full code : https://www.kaggle.com/anmolarora013/recognizinghandwrittendigits-svm
Intuition for Better Understanding of Pipelines in ML
Tags: Data Science, Machine Learning Ai, Pipeline As Code, Scikit Learn, Intuition

INTRODUCTION

Hey there! In this post we are going to be discussing about the what, why and how does a pipeline work while creating an ML model , there are some functionalities related to pipelines which aren't discussed in this post, but the main intent of this post is to develop an intuitive way to better understand ML pipelines, if you want a complete depth of this topic then you can refer the documentation here https://scikit-learn.org.

Any given ML project has several steps in it, right from extracting the correct data from various sources (aka data mining), converting and transforming that data into a dataset (aka data pre-processing), selecting the right model for the dataset (aka model selection), training and testing the dataset to the selected model (aka data modelling), checking the performance of the model (aka model evaluation), and lastly deploying the model to a working platform (aka model deployment).

All these steps can be quite complex sometimes and can be harder to maintain the track of, and hence there can be problems while code reproducibility, and code debugging.

We will take a step at a time to understand what are pipelines, then we will see why these are useful and lastly we will try to understand how these actually work, I hope you will gain some more insights and enjoy reading this post.

WHAT ARE PIPELINES?

Going by the definition, a pipeline is :

pipelines are used to help automate machine learning workflows. They operate by enabling a sequence of data to be transformed and correlated together in a model that can be tested and evaluated to achieve an outcome, whether positive or negative

Well, speaking in layman’s terms a pipeline can simply be thought of a ‘black box’ which takes in the input data and gives an output considering all the steps which we do in a straightforward approach (we will be discussing about these inputs and outputs and the inner workings later in this post).

For a better understanding of this, let’s consider an example where we use the California housing dataset provided by scikit-learn library, for our requirement,let’s consider that we want to scale the data (aka features) of that dataset first and then add polynomial features to it and at lastly predict a value (aka target) using an predictor class based on the scaled-polynomial transformed dataset. (Note that all the above steps are implemented using scikit-learn library)
Clustering con Gaussian Mixture Model en SKlearn y sus parámetros aplicado al Marketing.
By: Rubiales Alberto
Tags: Python, Marketing, Mathematics, Scikit Learn, Clustering

Data Scientist who loves technology and business. Always learning and sharing ideas. Working at GFK as a Data Scientist. https://www.patreon.com/arubiales
WhichBenefit:. Multi-Class Classification of Customer…
By: Pierre Winter
Tags: Consulting, Machine Learning, Classification, Scikit Learn, NLP

PRE-PROCESSING AND FEATURE ENGINEERING—

In order to engineer my features, I treated each feature independently and tried to extract as much useful information from them as possible.

Cents: This numerical feature is the USD amount in cents for each transaction. Negative values indicate that the purchase was a credit purchase and positive values indicate a debit purchase, where the amount was then transferred to or from the Benepass credit card. I chose to scale the data to unit variance by dividing each value by the standard deviation, such that the positive and negative USD amounts stay positive and negative, respectively. (Hint: This scaling is also important since I use regularization during the modeling stage).

Merchant_category: This categorical feature is the VISA Merchant Category Classification (MCC) code which describes the type of merchant at the point of transaction. Of the hundreds of MCC codes, only 53 were found in this dataset and so I OneHot encoded them into a sparse matrix as shown here (where each merchant category now becomes its own column):

OneHot encoding of the merchant_category feature

While this does add 53 columns to my feature list, these features span a large range of business types that offer benefits; and I expect these features to be generalizable such that new transactions fall within these categories.

In the rare event that a transaction enters the pipeline with a completely new merchant category, it can be flagged for manual inspection and potentially added as a feature for the next iteration of the model! 🔁

Merchant_name: This text feature is the name of the merchant as it appears on a VISA transaction record which is, funny enough, often times hard to understand even for the credit card owner. To clean this data, I chose to remove punctuation marks and numerical characters that were not directly part of the merchant name. I then turned this resulting string into a list of lowercase tokens to break down the merchant name into keywords.

🔧 Now comes the actual feature engineering! Using merchant name keywords and vectorizing (or OneHot encoding) them into columns is neither meaningful nor scalable. I also found that on a first pass through a ML classification model, the model had difficulty differentiating between health-related merchants such as pharmacies and doctor clinics. So instead I chose to create benefit-specific keywords from the training set such that companies with similar names tend to be classified into similar benefit categories.
Project ii -Recognising Hand Written Digits
By: Mohammad Ikram
Tags: Svc, Data Analytics, Digits Recognition, Scikit Learn, Python3

#Step 1 Importing Libraries

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets

from sklearn import svm

#Step 2 i.Data sets load ii.Shape finding

digits= datasets.load_digits()

digits.data.shape

(1797, 64)

#Step 3 Using matplotlib finding gray scale image

plt.figure(figsize=(20,4))

for index, (image,label)in enumerate(zip(digits.data[0:6],digits.target[0:6])):

plt.subplot(1,6,index+1)

plt.imshow(np.reshape(image,(8,8)),cmap = plt.cm.gray_r)

plt.title(f’Training: {label}’, fontsize = 20)

#Step 4 Train the Support Vector Classification (SVC) estimator

svc = svm.SVC(gamma=0.001,C=100.)

svc.fit(digits.data[1:1790], digits.target[1:1790])

SVC(C=100.0, gamma=0.001)

#Step 5 Test our estimator.

#lets predict for 5 data

pred = svc.predict(digits.data[1791:1796])

pred

array([4, 9, 0, 8, 9])

#Step 6 Validate

digits.target[1791:1796]

array([4, 9, 0, 8, 9])

#We can see that the value of pred and digits.target is same i.e ([4,9,0,8,9]) we conclude our model is 100% accurate.

#Step 7 checking more data set:

pred_1 = svc.predict(digits.data[1591:1596])

pred_1

array([0, 0, 2, 2, 7])

#Step 8 Validate

digits.target[1591:1596]

array([0, 0, 2, 2, 7])

#Step 9 checking more data set:

pred_2 = svc.predict(digits.data[1084:1091])

pred_2

array([2, 6, 3, 3, 7, 3, 3])

#Step 10 Validate

digits.target[1084:1091]

array([2, 6, 3, 3, 7, 3, 3])

#Step 11 checking more data set:

pred_3 = svc.predict(digits.data[184:195])

pred_3

array([2, 0, 1, 2, 6, 3, 3, 7, 3, 3, 4])

#Step 12 Validate

digits.target[184:195]

array([2, 0, 1, 2, 6, 3, 3, 7, 3, 3, 4])

#Step 13 By SVC checking the accuracy of our model

#i.Split data into Train and Test using train_test_split()

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test = train_test_split(digits.data,digits.target,test_size = 0.25, random_state=0)

#Step 14 putting data into SVC model

svc.fit(x_train,y_train)

SVC(C=100.0, gamma=0.001)

#Step 15 on test data checking data prediction

pred_acc = svc.predict(x_test)

pred_acc

#Step 16 Accuracy checking

from sklearn.metrics import accuracy_score

score = accuracy_score(y_test,pred1)

print(f’Accuracy = {round((score*100), 2)}%’)

Accuracy = 99.88%

#Accuracy of our model is 99.88% which is great
Recognizing Handwritten Digits
Tags: Machine Learning, Model Training, Scikit Learn, Python3, Blog

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Classifying handwritten text or numbers is important for many real-world scenarios. For example, a postal service can scan postal codes on envelopes to automate the grouping of envelopes which has to be sent to the same place.

For this article, we are going to use the ‘Digit’ data-set from ‘Scikit-Learn’

Scikit-Learn is a library for Python that contains numerous useful algorithms that can easily be implemented and altered for classification and other machine learning tasks.

You can refer the documentation of Scikit-Learn from the mentioned link:

https://scikit-learn.org/stable/

An estimator that is useful in this case is sklearn.svm.SVC, which uses the technique of Support Vector Classification (SVC).

For this task, you can use Jupyter Notebook or Google Colab.

Implementation

Importing basic python libraries:

Python libraries we are going to use are: Pandas, Numpy,sklearn, Matplotlib

Imports are as follows:

Basic imports

2.Loading Data-Sets and finding out its shape:

3.Obtaining gray-scale image of data using matplotlib library:

Here I will display only 6images. But you can display as many as you want

Code to display the first 6 data as images

The output of the above code will be:
Machine Learning with Julia / ScikitLearn.jl
By: Erdal Sönük
Tags: Scikitlearn, Machine Learning, Julia, Classifier, Iris

Popular packages for both Python and Julia.

My Julia adventure began with a university course. While this course, actually, I didn’t like Julia, as a Python Coder. After that, I wanted to give a chance for it. So, I started to research Julia coding and Julia packages. And than, I decided to share this post with you.

Julia

In this article, my purpose is preparing a Julia Notebook to create some classifier models for IRIS dataset.

Firstly, We have some special packages data processing and modelling.

ScikitLearn package is for modelling part, RDatasets package is for IRIS dataset loading and DataFrames package is for data processing.

packages

Next, if we decide to use which models on this data, we can add importing block.

scikit learn import models

Then, we are loading IRIS dataset and also, defining features and target. Next, we are preparing master data.

So, we are ready to design a model object. For this step, we will use struct type. Struct is very useful if you want to combine some variables in one object. Also, C programming language has it.

struct

At this time, we have a struct object. We can initiate it with appropriate parameters.

initial function for the struct

This object have our model (default parameters), nevertheless, if we don’t use it on a data, it is not meaningful. So, we need a function for usage of struct object like below.

usage function for the struct

Finally, our model system came the end of the flow.

Let’s try them!..

model test predicition accuracies

For other model algorithms, ScikitLearn has many more packages. These are could import with sk_import.

@sk_import ... svm : SVC

ensemle : AdaBoostClassifier

naive_bayes: GaussianNB

neighbors: KNeighborsClassifier

preprocessing: StandardScaler

References
Thanks for the info , but I think that you did not give direct answer , which is the target of any…
By: Abdullah Al Hommada
Tags: Scikit Learn, Machine Learning

Thanks for the info , but I think that you did not give direct answer , which is the target of any one read your article .

According to my very humble experience, Scikit learn should be the first step.
Machine Learning: Some notes about Cross-Validation
By: Papa Moryba Kouate
Tags: Data Science, Machine Learning, Statistics, Scikit Learn, Towards Data Science

Part I: K-fold cross-validation

K-fold cross-validation is one of the most used cross-validation methods. In this method, k represents the number of experiments(or fold) that I want to try in order to test and train my data.

For example, suppose that we want to make 5 experiments(or performance) with our data composed of 1000 records. So during the first experiment, we test or validate the first 200 records and then we train the remaining 800 records. When the first experiment is finished I obtain a certain accuracy. Then we make a second experiment where we test the 200 records that follow the previous records that I have tested and we train the remain 800 records.

I continue the experiment with a group of 200 records until I complete all the 5 experiments. Remember that we test always the next 200 records of the previous ones and train the remains records until the end.

At the end of my 5 experiments, I will obtain 5 accuracies and the final accuracy will be the mean of all accuracies.

Below you can see a visual example.

image by author

Cross-validation is implemented in scikit-learn using the cross_val_score function from the model_selecion module. The parameters of the cross_val_score functions are the model we want to evaluate, the training data, and the target. Below an example with Logistic Regression:
The Data Science & Machine Learning Bootcamp in Python
By: Derrick Mwiti
Tags: Python, Data Science, Machine Learning, Scikit Learn, Deep Learning

Hello, my name is Derrick Mwiti, I currently teach Data Science,Machine Learning, and Deep Learning to thousands of students on the internet. In this article, I’ll show you how you stand to benefit by taking my data science course.

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Tracking Sleep using Your Phone and Machine Learning
By: Bima Jenie
Tags: Python, Machine Learning, Sleep Tracking, Scikit Learn, Android

The code project can be found on https://github.com/josephbima/sleep-tracker

I’ve always wondered the exact time I actually spend sleeping at night. So when I needed ideas for the final project of my Mobile Health and Sensing class, I knew making a sleep tracking application was on the top of my list. So that is what me and my partner decided to do; to use our Android phone and a bit of machine learning to determine if the user is sleeping or not.

To track our sleep, we used the phone’s tri-axial accelerometer to determine the user is sleeping or in a vigil state. The accelerometer serves as an indicator on how much movement there is on the phone. The more movement there is, the more likely the user is not sleeping, and vice versa. We will use hourly intervals and determine how much of that hour is spent sleeping.

Data Collection

So first of all, we need to collect some data. We used an Android phone to do this, because it is much more easier to use its accelerometer and retrieve its data compared to using an iOS phone.

We used two methods of collecting data; real-time and simulated. We collected our real-time data by putting our phones in our pockets and going to bed as normal. We composed a rough sleeping log in which we tried to write down at what time we would go to bed and when we would wake up.

We wanted to strictly use real-sleep data because we feel it would result in a better activity recognition. However, there are two main problems that came up during this process of collecting data. The first one is the phone stops collecting data after 4 hours and this could be seen when the final timestamp of the generated CSV file was hours before we actually woke up, making us lose half or more than half of our collected data.

Another problem that we encountered is that it becomes increasingly difficult to know the exact time we fall asleep. We knew it wouldn’t be precise but more often than not we just end up guessing to estimate the time when we were tossing and turning and when we were falling asleep. We did this process of data collection for 4 or 5 nights before moving on to another method, because of the problems that would require a lot of work to solve.
Let’s Learn ML
By: Divyosmi Goswami
Tags: Pandas, Numpy, Scikit Learn, Data Scie, Tutorial For Beginners

Our first simple problem

you are given a task to find the price of land given sq. miles.

you have got a piece of simple data with sq. miles and price and now you have to find the price of the land given the square miles

sq. miles, price 100, 20

200, 40

400, 60

500, 80

python provides 3 fantastic libraries namely NumPy, pandas, Scikit-Learn

NumPy helps with algebra, pandas help with reading data, and editing and formatting, and Scikit-Learn helps in making predictive models and machines.

So, let’s start some coding for the problem.

import pandas as pd

import sklearn.linear_model.linear_regression as model

import numpy as np

# the most common data file types are .csv, .tsv, and .xlsx / .xls

# pandas provides classes for reading these our file is in format .csv so we will use pd.read_csv("data.csv") here

df = pd.read_csv("data.csv")

df.head(1) #show a small batch of data here only 1 row but as our data is small we dont need it

df.columns # shows the columns here sq. miles, price

# every model / function has a dependent and independent value that is the f(x) = y

x = df["sq. miles"] # the independent variable or on which the out put depends

y = df.drop(["sq. miles"], axis=1)# dependent variable and drop function drops a row when axis = 0 and column when axis = 1

lin_reg_model = model() # a linear regression predicts growth and fall or a particular value given 1 or more instances

# an sklearn linear regression model consists of 3 important functions fit(for fitting data), predict(for predicting data), score(to find how accurate or correct your model is)

lin_reg_model.fit(x,y)

# now model is ready to be tested

lin_reg_model.predict([10]) #meaning of this line is when the sq. miles is 10 what will the price be here it will be 2 but the result will not be 2 it will be very very close to 2 like 1.982377 but not 2

# now lets predict all the values

pred = lin_reg_model.predict(x)

lin_reg_model.score(y,pred) # in linear regression score works in this way it at first subtracts the pred from y and then squares it and finds the average or mean, that means all the value in y is subtracted by the pred and then the whole array is squared and you get all the values of the array squared and then finding average of the whole array.

And voila solved the first problem feeling proud of yourself to wait there is more to cover and solve. feeling the breeze through your hair and power in hand I know and it will be more fun while you solve the first exercise that you will get at the end of the blog.
Hepatit-C Sınıflandırma Problemi
By: Burak Uğur
Tags: Python, Data Science, Machine Learning, Classification, Scikit Learn

burakugurr/AI-ML-DL

Permalink Dismiss GitHub is home to over 50 million developers working together to host and review code, manage…
Ev Fiyatlarının Regresyon Modelleri ile Tahmini
By: Alican Şahi̇n
Tags: Dataset, Kaggle, Data Science, Scikit Learn, Regression Data Analysis

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
Adaptive Boosting, Simply Explained through Python
By: Vagif Aliyev
Tags: Python, Machine Learning, Artificial Intelligence, Classification, Scikit Learn

Ok, so bearing that in mind, let’s uncover the algorithm:

First, AdaBoost gives each instance in a training set a certain weight(this is different from the predictor’s weight). To begin with, each instance is given an equal weight. More specifically, the base weight is usually always 1/m (m being the number of training instances). After the instance weights have been added, AdaBoost trains the first weak learner on the data. From the predictor’s performance, it calculates the predictor’s weighted error rate, or the amount of errors it made. The formula for that is the following(point 1):

3. After that, it calculates the predictor’s weight, using the error rate calculated before(point 2), with ln being the learning rate hyperparameter. The higher its weight, the better it is, and the more its say in the final prediction. If the predictor is close to randomly guessing, then its weight will be close to 0. If it is worse that random guessing, then its weight will be negative.

4. Next, the instance weights are updated(using the formula on point 3). The instances that were incorrect are boosted, and the other weights are decreased, and then normalised to that they cumulatively equate to 1.

5. A new predictor is trained on the new data with the updated weights, and the process is repeated. The algorithm stops when the desired number of predictors have run, or the perfect predictor is found.

6. To make predictions, AdaBoost computer the predictions of all the weighted predictor and calculates which class got the most weighted votes.
What is Machine Learning? A Simple Guide
By: Krishnan Navadia
Tags: Python, Data Science, Machine Learning, Artificial Intelligence, Scikit Learn

Introduction

What is Machine Learning: Machine learning is the process of teaching a computer system on how to make accurate predictions when fed data.

Let’s think of this definition with an example.

What is Machine Learning? Well, we have the world, and in the world we have humans and we have computers and one of the main differences between humans and computers is that humans learn from past experiences Whereas computers need to be told what to do, they need to be programmed.

And now the question is, can we get computers to learn from the experience too? And the answer is yes, we can. And that is precisely what machine learning is.

Teaching computers to learn to perform tasks from past experiences. Of course, for computers, past experiences are just recorded as data.
5x Faster Scikit-Learn Parameter Tuning in 5 Lines of Code
By: Michael Chau
Tags: Python, Data Science, Machine Learning, Scikit Learn, Deep Learning

* Note: For smaller datasets (10,000 or fewer data points), there may be a sacrifice in accuracy when attempting to fit with early stopping. We don’t anticipate this to make a difference for users as the library is intended to speed up large training tasks with large datasets.

Simple 60 second Walkthrough

Let’s take a look at how it all works.

Run pip install tune-sklearn ray[tune] or pip install tune-sklearn "ray[tune]" to get started with our example code below.

Hyperparam set 2 is a set of unpromising hyperparameters that would be detected by tune’s early stopping mechanisms, and stopped early to avoid wasting training time and resources.

TuneGridSearchCV Example

To start out, it’s as easy as changing our import statement to get Tune’s grid search cross validation interface:

And from there, we would proceed just like how we would in Scikit-Learn’s interface! Let’s use a “dummy” custom classification dataset and an SGDClassifier to classify the data.

We choose the SGDClassifier because it has a partial_fit API, which enables it to stop fitting to the data for a certain hyperparameter configuration. If the estimator does not support early stopping, we would fall back to a parallel grid search.

As you can see, the setup here is exactly how you would do it for Scikit-Learn! Now, let’s try fitting a model.

Note the slight differences we introduced above:

a new early_stopping variable, and a specification of max_iters parameter

The early_stopping determines when to stop early — MedianStoppingRule is a great default but see Tune’s documentation on schedulers here for a full list to choose from. max_iters is the maximum number of iterations a given hyperparameter set could run for; it may run for fewer iterations if it is early stopped.

Try running this compared to the GridSearchCV equivalent.

TuneSearchCV Bayesian Optimization Example

Other than the grid search interface, tune-sklearn also provides an interface, TuneSearchCV, for sampling from distributions of hyperparameters.

In addition, you can easily enable Bayesian optimization over the distributions in TuneSearchCV in only a few lines of code changes.

Run pip install scikit-optimize to try out this example:

Lines 17, 18, and 26 are the only lines of code changed to enable Bayesian optimization

As you can see, it’s very simple to integrate tune-sklearn into existing code. Check out more detailed examples and get started with tune-sklearn here and let us know what you think! Also take a look at Ray’s replacement for joblib, which allows users to parallelize training over multiple nodes, not just one node, further speeding up training.

Documentation and Examples
We Can Be Heroes movie review: Priyanka Chopra made her way through Robert Rodriguez’s silly superhero film – Hollywood
By: Hitesh Agesaniya
Tags: Los Angeles, Superheroes, Priyanka Chopra, Mumbai

in The New York Times
Hire Medical Malpractice Attorney In Los Angeles
By: Armond Marcarian
Tags: Attorney, Losangeles, Lawyers

If you or a loved one has been injured or died as a result of medical malpractice, you may be very confused about what steps to take next. At the Marcarian Law Firm, P.C. an experienced Los Angeles medical malpractice attorney Armond Marcarian can help you understand what happened leading up to your injury or the death of your loved one.
We climbed to the top of the hill, to get closer to the sky.
By: Jodi Ryan
Tags: Poetry, Christmas Star, Los Angeles, Winter Solstice, Nature Photography

sources of light

‘Christmas Star’ in the southwest sky.

We climbed to the top of the hill, to get closer to the sky.

If words were fire dancers, I could describe

the morphing colors along our ascent, and I’d leave out the part

where the dog stops to do his earthly business

next to some sweet skygazers,

but by now, our city of fallen angels

knows juxtaposition

of miracles and waste matters, life and death,

patience and wandering stars.

The light was mesmerizing.

What I’ll remember most are the faces

gazing up with naked eyes

searching sky to skyline,

pausing on the shimmering sprawl

of stories, real estate, grass, landmarks, dreams, deadlines, galaxies,

naked eyes, making out

what we could

on an impossibly clear night

seeking what light we could

on an impossibly clear night,

the epicenter

of our darkest day

begging for hindsight.

Here now, with shadows, light,

where our dismantled hearts

could rest, and remember that

some things, also not in our control,

are divine and good.

On December 21, 2020, we got to witness the ‘Christmas Star’

which isn’t a star at all, but actually two planets coming together,

creating brightness. I made a wish anyway.