Linear-Regression-Scratch

📊 Linear Regression From Scratch — Full Math & NumPy Implementation This repo is my full implementation of multiple linear regression from scratch using only NumPy. It includes full mathematical derivations, practical Python code,Fully explanted jupyter Notebook and PDF notes. All as part of my journey studying and documenting Machine Learning. 📋 Table of Contents Topics Covered Installation & Usage How to Run References 📐 Topics Covered Core Math & Implementation Mean Squared Error (MSE) Ordinary Least Squares (OLS) derivation Gradient Descent Statistical Inference Standard Error of $(\hat\beta)$ Confidence Intervals for coefficients F-test & T-test procedures Model Evaluation Residual Standard Error (RSE) R‑Squared (R²) metric Prediction Intervals Assumptions & Diagnostics Linearity check Heteroscedasticity detection Residual Analysis Practical Challenges Handling Outliers Identifying High Leverage Points Dealing with Multicollinearity 💻 Installation & Usage Clone the repo Install dependencies bash Run The Notebook jupyter notebook LinearRegressionScratch.ipynb Or Python Script python LinearRegressionImplementation.py 📚 References