LinearRegressionProject/
├── data/                        # Store dataset files here
│
├── models/
│   ├── linear_regression.py     # OLS, Batch GD, SGD, Mini-Batch GD
│   ├── regularization.py        # Ridge (L2), Lasso (L1)
│   ├── scaling.py               # Standardization, Normalization
│   └── encoding.py              # One-Hot and Label Encoding
│
├── metrics/
│   └── evaluation.py            # Evaluation metrics: R², MSE
│
├── notebook/
│   └── LinearRegressionDemo.ipynb  # Jupyter Notebook for step-by-step usage
│
├── main.py                      # Script to train/test model and run pipeline
├── README.md                    # Project overview, instructions, and structure

This project is a complete implementation of Linear Regression from scratch using NumPy, designed to be clean, modular, and educational. It covers everything from preprocessing and encoding to regularization and different gradient descent techniques — all without using any machine learning libraries like scikit-learn for the model itself.

• 📈 Optimization Methods

  • OLS (Ordinary Least Squares)
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Mini-batch Gradient Descent

• 🧩 Regularization

  • L1 Regularization (Lasso)
  • L2 Regularization (Ridge)

• 🔧 Preprocessing

  • Standardization
  • Normalization

• 🎛️ Encoding

  • One-Hot Encoding
  • Label Encoding

• 📏 Evaluation Metrics

  • R² Score
  • Mean Squared Error (MSE)

This project is built for learning and experimentation. Instead of relying on external ML libraries, every step — from optimization to regularization — is written from scratch using only NumPy. It’s a great resource if you're learning:

• The math behind regression
• How regularization affects optimization
• How preprocessing affects learning
• How to structure ML code modularly