The Home Price Prediction App is a web application designed to provide users with an estimate of home prices based on various input features. This app leverages a machine learning model, specifically a linear regression algorithm, trained on historical home price data to make accurate predictions.
- Predict Home Prices: Input various home features to get a predicted price.
- User-Friendly Interface: Simple and intuitive interface for easy user interaction.
- Flask Backend: Efficient backend server for handling prediction requests.
- Machine Learning Model: Linear regression model trained on historical data for accurate price predictions.
To run this project locally, follow these steps:
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Clone the repository:
git clone https://github.com/your-username/home-price-prediction.git cd home-price-prediction -
Create a virtual environment:
python3 -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Install the required packages:
pip install -r requirements.txt
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Run the Flask app:
flask run
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Open your browser and navigate to:
http://127.0.0.1:5000/
- Enter the required home features (e.g., location, size, number of bedrooms) in the form on the main page.
- Click the "Predict" button.
- The predicted home price will be displayed on the page.
The linear regression model was trained using historical home price data. The training script (train_model.py) preprocesses the data, trains the model, and saves it as a pickle file (model.pkl).
Contributions are welcome! Please fork this repository and submit a pull request with your changes.
This project is licensed under the MIT License. See the LICENSE file for details.