📊 Machine Learning Algorithm Visualizations

Tech stack: Python, NumPy, Pygame

Overview This project is an interactive visualization tool for understanding the mechanics of core machine learning algorithms. Instead of relying on libraries like scikit-learn, I implemented the algorithms from scratch to provide an educational and transparent look at how they work under the hood.

Features

Implemented and visualized multiple ML algorithms:

Polynomial Regression – Gradient descent with real-time curve fitting.

Logistic Regression – Classification boundary visualization.

k-Means Clustering – Dynamic cluster assignment and centroid updates.

Anomaly Detection – Outlier identification with visualization overlays.

Real-time updates using Pygame for intuitive visual learning.

Adjustable parameters (e.g., polynomial degree, cluster count, anomaly thresholds).

Exportable equation outputs (copy-paste ready for tools like Desmos).