Best 5 Logistic Regression GitHub Projects for Beginners
Tags: machinelearning, tutorial, opensource, algorithms
Published: 2023-01-15 19:22:06

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. 

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/zbp2fupq342ex5vvfm3h.png)

## 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).](https://en.wikipedia.org/wiki/Logistic_regression)

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](https://www.datacamp.com/?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAW1rSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK) allows me to learn without limits.

[Datacamp](https://www.datacamp.com/?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAW1rSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK) allows you to take courses on your own time and learn the fundamental skills you need to transition to a successful career.

[Datacamp](https://www.datacamp.com/?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAW1rSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK) has taught me to pick up new ideas quickly and apply them to real-world problems. While I was in my learning phase, [Datacamp](https://www.datacamp.com/?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAW1rSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK) 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.

1. [Machine Learning with PySpark](https://www.datacamp.com/courses/machine-learning-with-pyspark?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAQ5rSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK)
2. [Linear Classifiers in Python](https://www.datacamp.com/courses/linear-classifiers-in-python?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAQ9rSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK)
3. [Introduction to Regression in R](https://www.datacamp.com/courses/generalized-linear-models-in-r?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAVxrSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK)
4. [Generalized Linear Models in R](https://www.datacamp.com/courses/generalized-linear-models-in-python?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAVxrSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK)
5. [Generalized Linear Models in Python](https://www.datacamp.com/courses/generalized-linear-models-in-python?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJAVxrSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK)
6. [Intermediate Regression in R](https://www.datacamp.com/courses/intermediate-regression-in-r?irclickid=whuVehRgUxyNR6tzKu2gxSynUkAwJF3NrSDLXM0&irgwc=1&utm_medium=affiliate&utm_source=impact&utm_campaign=000000_1-2871519_2-mix_3-all_4-na_5-na_6-na_7-mp_8-affl-ip_9-na_10-bau_11-mrinalwalia&utm_content=ONLINE_TRACKING_LINK)

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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](https://cs.brown.edu/people/acrotty/pubs/Galakatos2017_ReferenceWorkEntry_DistributedMachineLearning.pdf) 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:_
1. GBDT(Gradient Boosting Decision Trees)
2. GBRT(Gradient Boosting Regression Trees)
3. Mixture Logistic Regression
4. Gradient Boosting Soft Tree
5. Factorization Machines
6. Field-aware Factorization Machines
7. Logistic Regression
8. Softmax

_There are a lot of features that ytk-learn provides:_
1. Local file system and hdfs file system support
2. Easy and user-friendly codes for online production 
3. Efficiently runs with Java SE Runtime Environment 8  without any complex installation

_Here are some valuable resources for you to access:_
1. Running Guide
2. Demo
3. Model Introduction
4. Data Format
5. Evaluation Metrics
6. Performance Guide
7. Online Prediction Guide

## 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)](https://stanford.edu/~boyd/admm.html) 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](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud) and was gathered in Europe in 2 days in Sept 2013.

_The three models implemented are:_
1. Logistic Regression
2. K-Means Clustering
3. 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)](http://hdt%20%28hadoop%20development%20tools%29/) plugin installed.

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