Best 5 Logistic Regression GitHub Projects for Beginners

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

5 min readJan 15, 2023

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

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.

Courses from DataCamp

Learning isn’t just about being more competent at your job, it is so much more than that. Datacamp allows me to learn without limits.
Datacamp allows you to take courses on your own time and learn the fundamental skills you need to transition to a successful career.
Datacamp has taught me to pick up new ideas quickly and apply them to real-world problems. While I was in my learning phase, Datacamp hooked me on everything in the courses, from the course content and TA feedback to meetup events and the professor’s Twitter feeds.
Here are some of my favorite courses I highly recommend you to learn from whenever it fits your schedule and mood. You can directly apply the concepts and skills learned from these courses to an exciting new project at work or at your university.

Machine Learning with PySpark

Linear Classifiers in Python

Introduction to Regression in R

Generalized Linear Models in R

Generalized Linear Models in Python

Intermediate Regression in R

Coming back to the topic -

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

Here are some valuable resources for you to access:

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 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