📈 Ridge Regression on Housing Dataset

🔹 Project Overview

This project implements Ridge Regression using scikit-learn to predict house prices from a housing dataset. Ridge Regression is a regularized version of Linear Regression that helps reduce overfitting by penalizing large coefficients.

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

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📂 Repository Contents

Ridge_Regression
│
├── Ridge_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 a Ridge 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 a Ridge 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: 0.6397684336089735

Interpretation:
The model explains approximately 64% of the variance in housing prices. Ridge regularization helps control model complexity while maintaining performance comparable to standard linear regression.

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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
• Regularization improves model stability

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📌 Key Observations

• Ridge Regression provides a more stable alternative to Linear Regression
• Regularization helps reduce overfitting
• Performance remains strong while controlling coefficient magnitude

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▶️ How to Run the Project

1. Clone the repository

git clone https://github.com/btboilerplate/Ridge_Regression.git  

2. Install required libraries

pip install numpy pandas matplotlib scikit-learn  

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

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🚀 Future Enhancements

• Compare Ridge vs Lasso vs ElasticNet
• Tune the alpha hyperparameter using cross-validation
• Add RMSE and MAE evaluation metrics

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