📈 ElasticNet Regression on Housing Dataset

🔹 Project Overview

This project implements ElasticNet Regression using scikit-learn to predict house prices from a housing dataset. ElasticNet combines L1 (Lasso) and L2 (Ridge) regularization, balancing feature selection and coefficient stability, making it effective when features are correlated.

The notebook demonstrates the complete machine learning workflow, including data loading, preprocessing, model training, evaluation, and residual analysis.

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📂 Repository Contents

ElasticNet_Regression
│
├── ElasticNet_Regression.ipynb
├── housing.csv
├── residual_distribution.png
└── README.md

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📊 Dataset

• File: housing.csv
• Type: Tabular housing data
• Purpose: Used to train and evaluate an ElasticNet Regression model for house price prediction

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🛠️ Libraries & Tools Used

• Python
• NumPy
• Pandas
• Matplotlib
• scikit-learn

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⚙️ Project Workflow

1. Load the housing dataset
2. Perform train-test split
3. Train an ElasticNet Regression model
4. Predict house prices on test data
5. Evaluate model performance using R² Score
6. Analyze residual distribution

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📈 Model Evaluation

R² Score: 0.6398401841321038

Interpretation:
The model explains approximately 64% of the variance in housing prices. By combining L1 and L2 regularization, ElasticNet maintains strong predictive performance while improving model robustness and handling correlated features.

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📉 Residual Analysis

Residual Distribution (y_test − ridge_pred):

Key Insights

• Residuals are approximately normally distributed
• Indicates that regression assumptions are largely satisfied
• ElasticNet provides a balance between sparsity and stability

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📌 Key Observations

• ElasticNet combines the strengths of Ridge and Lasso regression
• Helps manage multicollinearity among features
• Provides stable performance with controlled model complexity

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▶️ How to Run the Project

1. Clone the repository

git clone https://github.com/your-username/ElasticNet_Regression.git  

2. Install required libraries

pip install numpy pandas matplotlib scikit-learn  

3. Open ElasticNet_Regression.ipynb
4. Run all cells sequentially

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🚀 Future Enhancements

• Tune alpha and l1_ratio using cross-validation
• Compare ElasticNet with Ridge and Lasso results
• Add RMSE and MAE evaluation metrics

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