📈 Linear Regression on Housing Dataset

🔹 Project Overview

This project implements Linear Regression using scikit-learn to predict house prices from a housing dataset. The notebook demonstrates the complete machine learning workflow, including data loading, preprocessing, model training, prediction, evaluation, and residual analysis.

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📂 Repository Contents

Linear_Regression
│
├── Linear_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 linear 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 Linear 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: 0.6395768324695243

Interpretation:
The model explains approximately 64% of the variance in housing prices, which is a reasonable result for a baseline linear regression model on real-world data.

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📉 Residual Analysis

Residual Distribution (y_test − reg_pred):

Key Insights:

• Residuals are approximately normally distributed
• Indicates that linear regression assumptions are largely satisfied
• Slight skewness suggests potential improvement with advanced models

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📌 Key Observations

• Linear Regression provides a strong baseline model
• Model performance can be improved using:
  • Feature engineering
  • Polynomial regression
  • Regularization techniques (Ridge, Lasso)
  • Tree-based or ensemble models

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▶️ How to Run the Project

1. Clone the repository

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

2. Install required libraries

pip install numpy pandas matplotlib scikit-learn

3. Open Linear_Regression.ipynb and run all cells sequentially

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🚀 Future Enhancements

• Add RMSE and MAE evaluation metrics
• Experiment with Polynomial Regression
• Apply feature scaling comparisons
• Try regularized regression models

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