Machine Learning Algorithms from Scratch

This project focuses on implementing a few fundamental Machine Learning (ML) algorithms, including Categorical Naive Bayes, mini-batch logistic regression, and the One vs Rest approach. The objective is to build these algorithms from scratch, along with essential preprocessing tools like train_test_split, One Hot encoding, and Standard scaler, using only Python’s numpy library. You can explore the complete code in my GitHub repository.