Linear Regression Models — Machine Learning

Regularized linear regression analysis on the Boston Housing dataset, comparing Linear Regression, Lasso (L1), and Ridge (L2) approaches.

Overview

This project predicts the median value of owner-occupied homes (MEDV) using multiple regression techniques:

Model	Approach
Linear Regression	Baseline (no regularization)
Lasso	Pipeline + GridSearchCV
Ridge	Pipeline + GridSearchCV
Ridge	Manual (RidgeCV)
Lasso	Manual (LassoCV)

The notebook includes data preprocessing (Box-Cox transformation for skewed features), exploratory data analysis, model training with cross-validation, and a final comparison table.

Setup

# Create virtual environment
python -m venv venv

# Activate (Windows)
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Project Structure

├── NoteBook.ipynb   # Main notebook
├── boston_housing_dataset.csv     # Dataset
├── requirements.txt              # Python dependencies
├── .gitignore
└── README.md

Tech Stack

• Python 3.11+
• pandas, numpy
• matplotlib, seaborn
• scikit-learn
• scipy