Welcome to the Machine Learning Models repository!
This project is a curated collection of foundational machine learning algorithms implemented from scratch using Python and NumPy, aimed at deepening understanding of how these models work under the hood.
This repository is designed for learners and practitioners who want to:
- Understand ML algorithms by building them from the ground up
- Explore mathematical intuition through code
- Strengthen implementation skills for interviews or academic work
No external machine learning libraries (like scikit-learn) are used—everything is implemented from scratch using basic Python libraries such as NumPy and Pandas.
- Decision Tree
- K-Nearest Neighbors (KNN)
- Multinomial Naive Bayes
- Neural Networks
- Random Forest