Best 5 Logistic Regression GitHub Projects for Beginners

Logistic regression is one of the most often discussed topics for Data Science interviews.

Learn about the best 5 projects in Logistic Regression on GitHub to add to your resume and ace your following Data Science interview.

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What is Logistic Regression?

In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model (the coefficients in the linear combination).

It is one of the most important and frequently asked topics for Data Science interviews. Logistic regression is easy to understand and is one of the widely adopted machine learning algorithms to categorize incoming data based on historical data.

Today's article will help you get familiar with some of the best and most important Logistic Regression open-source projects. All the projects are easily accessible on GitHub, and you can play around and customize them to your requirements. (Remember to review their licensing terms before you use them in your project.)

**Note:** In this article, we will discuss some fantastic open-source Logistic Regression Projects/Repositories that you can utilize in your projects in 2023. To read more about each, I recommend following the link given along the project.

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6. Intermediate Regression in R

Coming back to the topic -

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1. ytk-learn

GitHub: https://github.com/kanyun-inc/ytk-learn
Official Documentation: https://github.com/clips/pattern/wiki
GitHub Stars: 348
GitHub Forks: 81
Languages: Java(97.8%), Shell(1.7%), Other (0.5%)

Ytk-learn is an open-source distributed machine-learning library written in Java. It can help implement machine learning algorithms and run on single and multiple machines and other major distributed environments such as Hadoop and Spark. This library supports many operating systems like Linux, Windows, and Mac OS.

The different models supported by this library are as follows:

• GBDT(Gradient Boosting Decision Trees)
• GBRT(Gradient Boosting Regression Trees)
• Mixture Logistic Regression
• Gradient Boosting Soft Tree
• Factorization Machines
• Field-aware Factorization Machines
• Logistic Regression
• Softmax

There are a lot of features that ytk-learn provides:

• Local file system and hdfs file system support
• Easy and user-friendly codes for online production
• Efficiently runs with Java SE Runtime Environment 8 without any complex installation

2. MI Ease

GitHub: https://github.com/linkedin/ml-ease
Official Documentation: https://github.com/linkedin/ml-ease#readme
GitHub Stars: 328
GitHub Forks: 79
Languages: Java(100%)

MI Ease is an open-source and large-scale machine learning library in the Alternating Direction Method of Multipliers (ADMM) based on a large-scale logistic regression algorithm. It was invented by engineers from LinkedIn and is licensed under Apache Version 2.0 with copyright 2014 LinkedIn corporation.

The ADMM or Alternating Direction Method of Multipliers considers the large-scale logistic regression model as fitting as a convex optimization problem but with a constraint that the ADMM algorithm is about to converge but minimizing the user-defined loss function enforces an extra constraint that coefficients from all partitions have to become equal to solve this optimization problem by using an iterative process.

You can start the installation by using maven for compiling, and the command is mvn clean install

3. Zen

GitHub: https://github.com/cloudml/zen
Official Documentation: https://github.com/cloudml/zen#readme
GitHub Stars: 172
GitHub Forks: 75
Languages: Scala(98.9%), Others(1.1%)

Zen is an open-source library written in scala to provide the most enormous scale and efficient machine learning platform on top of spark and in logistic regression, latent Dirichlet allocation, factorization machines, and DNN.

It is inspired by Apache Spark, MLib, and GraphX, with very sophisticated optimizations and newly added features to optimize and scale up the machine learning training process.

Zen has a vision of combining data insights, ml algorithms, and system experience to achieve a successful machine-learning platform.

4. ML Fraud Detection

GitHub: https://github.com/georgymh/ml-fraud-detection
Official Documentation: https://github.com/georgymh/ml-fraud-detection/blob/master/paper.pdf
GitHub Stars: 170
GitHub Forks: 108
Languages: Jupyter Notebook(100%)

ML Fraud Detection is a repository that implements three models trained to label anonymized credit card transactions as fraudulent or actual. The dataset is taken from Kaggle's competition Credit Card Fraud Detection and was gathered in Europe in 2 days in Sept 2013.

The three models implemented are:

• Logistic Regression
• K-Means Clustering
• Neural Networks

In the future, the authors of this project also want to implement an autoencoder or try our hand at an SVM to see the performance. It will be beneficial to keep this repository forked and starred in the future.

5. Mlhadoop

GitHub: https://github.com/punit-naik/MLHadoop
Official Documentation: https://github.com/punit-naik/MLHadoop#readme
GitHub Stars: 52
GitHub Forks: 38
Languages: Java(100%)

MLhadoop is a GitHub repository written from scratch in Java that contains machine-learning MapReduce codes for Hadoop. All the codes are written in basic Maths, including algorithm prediction algorithms like linear and logistic regression - iterative version, clustering algorithms like k-means clustering, classification algorithms like KNN classifiers, MBA, familiar friends, etc.

The IDE used to implement the algorithms in Java is Eclipse IDE with Hadoop Development Tools (HDT) plugin installed.

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