📊 Ridge, Lasso, and ElasticNet Regression

This project demonstrates how to use regularized linear regression techniques — Ridge, Lasso, and ElasticNet — to build more robust machine learning models in Python. These methods help prevent overfitting and handle multicollinearity in datasets.

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📌 Project Overview

Regularized regression is an extension of linear regression that introduces a penalty term to shrink model coefficients. This improves generalization and model performance.

This notebook walks you through:

• Understanding Ridge, Lasso, and ElasticNet regression
• Implementing models using scikit-learn
• Comparing model performance
• Visualizing results
• Evaluating model metrics

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🧠 Concepts Covered

• Linear Regression refresher
• Ridge Regression (L2 penalty)
• Lasso Regression (L1 penalty)
• ElasticNet Regression (L1 + L2 combination)
• Feature selection with Lasso
• Model evaluation (MSE, R²)
• Cross-validation

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🛠️ Tools & Libraries

Tool/Library	Purpose
Python	Core programming language
Jupyter Notebook	Interactive coding environment
NumPy	Numerical operations
pandas	Data handling and manipulation
matplotlib	Data visualization
seaborn	Statistical visualization
scikit-learn	Machine learning models & utilities

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📁 Project Structure

File/Folder Name	Description
Ridge_Lasso_ElasticNet.ipynb	Main notebook with model implementation
README.md	Project documentation (this file)
Algerian_forest_fires_cleaned_dataset.csv / Algerian_forest_fires_dataset_UPDATE.csv	Dataset used for model training/testing

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🚀 How to Run

1. Clone the Repository

   git clone https://github.com/YourUsername/Ridge-Lasso-ElasticNet.git
   cd Ridge-Lasso-ElasticNet
   

2. Install dependencies (optional)

   pip install numpy pandas matplotlib seaborn scikit-learn
   

3. Launch Jupyter Notebook

   jupyter notebook
   

4. Open ipynb files and run the cells.