🏡 House Price Prediction using Linear Regression

📌 Project Overview

This project predicts house prices using a Linear Regression model built with Scikit-learn.

The model is trained using two input features:

• Area
• Number of Rooms

and predicts the house price.

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📂 Project Structure

House-Price-Prediction/
├── data/
│   └── house_price.csv
├── images/
│   ├── actual_vs_predicted.png
│   └── residual_plot.png
├── models/
│   └── house_price_model.pkl
├── notebook/
│   └── House_Price_prediction.ipynb
├── README.md
├── requirements.txt
└── .gitignore

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🛠 Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• Joblib
• Google Colab

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📈 Machine Learning Workflow

• Load the dataset
• Explore the data
• Select features and target
• Split the dataset into training and testing sets
• Train a Linear Regression model
• Predict house prices
• Evaluate the model
• Analyze residuals
• Save the trained model

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📊 Model Performance

Metric	Value
Mean Absolute Error (MAE)	72,334.75
Mean Squared Error (MSE)	8,610,424,544.78
R² Score	0.5149

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📷 Results

Actual vs Predicted

Residual Plot

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💾 Saved Model

The trained model is stored in:

models/house_price_model.pkl

It can be loaded using:

import joblib

model = joblib.load("models/house_price_model.pkl")

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🚀 Future Improvements

• Add more features such as bathrooms, garage, and location
• Try Decision Tree Regression
• Try Random Forest Regression
• Perform feature engineering
• Tune model hyperparameters

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👨‍💻 Author

Hamed Wahedi

Computer Science Student | Machine Learning & AI Engineer