🏡 Predicting Home Prices using Python and Linear Regression

📌 Overview

This project uses simple linear regression to predict housing prices based on square footage. It is a beginner-friendly machine learning model built with Python, following best practices in data preprocessing, model training, and evaluation.

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📂 Project Structure

predict-home-prices/
├── Project1.py         # Python script with full code
├── home_dataset.csv    # Dataset containing house prices and square footage
└── README.md           # Project documentation

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📊 Dataset

The dataset used (home_dataset.csv) contains two columns:

• area – Size of the house in square feet
• price – Price of the house in INR (lakhs)

This is a small synthetic dataset, ideal for learning the basics of linear regression.

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🔧 Tools and Technologies

• Python (3.x)
• Pandas
• NumPy
• Matplotlib
• Scikit-learn (for Linear Regression)

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🚀 How the Model Works

1. Load the dataset using Pandas
2. Visualize the relationship between house area and price
3. Train a linear regression model using scikit-learn
4. Predict price for a house with a given area
5. Plot the regression line over the scatterplot
6. Export model using joblib (optional)

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📈 Sample Output

Raw Data Plot (House Prices vs. Size): Shows the actual distribution of the dataset before model fitting.

Linear Regression Fit: Below is the linear regression output showing the relationship between house size (sq.ft) and price (millions $): The red line shows the predicted house prices based on the linear model.

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💡 Key Learnings

• Applied Linear Regression using scikit-learn
• Understood the relationship between variables (area vs. price)
• Practiced data visualization with Matplotlib
• Explored basic model evaluation and prediction

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🛠 Future Improvements

• Use multiple linear regression with more features (e.g., number of bedrooms, location)
• Implement model evaluation metrics like R² score and RMSE
• Deploy the model using Flask or Streamlit

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🧠 Credits

Based on the tutorial by Codédex