0xHadyy/LogisticLearn

Logistic Regression from Scratch - NumPy implementation with L1 and L2 ,cross-validation, Grid-Search, and sklearn benchmarks. Complete math derivations + code

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/ 100
Emerging

This project offers a deep dive into binary logistic regression, a fundamental machine learning algorithm. It provides a complete, step-by-step implementation in Python using NumPy, covering core model concepts, regularization, and model evaluation techniques. Data scientists and machine learning engineers can use this to understand the underlying mathematics and build logistic regression models from first principles.

Use this if you are a data scientist or machine learning engineer who wants to understand, implement, and fine-tune binary logistic regression models from scratch, rather than just using off-the-shelf libraries.

Not ideal if you primarily need to quickly apply pre-built machine learning models without understanding their internal mechanics, or if you're working with extremely large datasets where pure NumPy might be too slow.

predictive-modeling binary-classification machine-learning-fundamentals model-evaluation algorithm-implementation
No License No Package No Dependents
Maintenance 6 / 25
Adoption 6 / 25
Maturity 7 / 25
Community 13 / 25

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24

Forks

4

Language

Jupyter Notebook

License

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Category

linear-regression-implementations

Last pushed

Oct 22, 2025

Commits (30d)

0

GitHub
Linear Regression Implementations · 58 frameworks

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