Real Estate Price Prediction Website

This project demonstrates the process of building a real estate price prediction website. We start by developing a machine learning model using sklearn and linear regression, leveraging the Bangalore home prices dataset from Kaggle.com.

Project Components

1. Model Building with sklearn

   • Utilize linear regression to predict home prices based on features like square footage, number of bedrooms, etc.
   • Cover data loading, cleaning, outlier detection, feature engineering, dimensionality reduction, and hyperparameter tuning using GridSearchCV.

2. Python Flask Server

   • Develop a Flask server to host the trained model.
   • Handle HTTP requests from the frontend to predict home prices.

3. Website Development

   • Create a frontend using HTML, CSS, and JavaScript.
   • Allow users to input home features and retrieve predicted prices from the Flask server.

Technologies and Tools Used

• Python: Main programming language.
• Numpy and Pandas: Data cleaning and manipulation.
• Matplotlib: Data visualization.
• Sklearn: Machine learning model building.
• Jupyter Notebook, Visual Studio Code, PyCharm: IDEs used for development.
• Flask: Python micro web framework for HTTP server.
• HTML/CSS/JavaScript: Frontend development for user interface.

Setup Instructions

1. Clone the Repository

git clone <repository_url>
cd real-estate-price-prediction 

2. Install Dependencies pip install -r requirements.txt

3. Run Flask Server python app.py

4. Open the Website

• Navigate to http://localhost:5000 in your web browser.

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Data source: Bangalore home prices dataset on Kaggle
• Inspiration and guidance from codebasics.io.