HomeValueAI is a machine learning project that predicts the value of residential properties based on various features such as location, size, number of bedrooms and bathrooms, and more. The project uses state-of-the-art algorithms and techniques in machine learning to accurately estimate the value of a home, providing valuable insights to homeowners, real estate professionals, and investors.
To get started with HomeValueAI, you'll need to clone this repository to your local machine and install the necessary dependencies. You can do this by running the following commands:
git clone https://github.com/your_username/HomeValueAI.git
cd HomeValueAI
pip install -r requirements.txt
Once you have the project and its dependencies installed, you can run the home_value_prediction.py script to predict the value of a home based on its features. The script takes a CSV file as input, where each row represents a property and each column represents a feature of the property.
python home_value_prediction.py --input_file input.csv --output_file output.csv
The output file will contain the predicted value for each property in the input file.
If you'd like to contribute to the development of HomeValueAI, you can do so by submitting issues or pull requests on GitHub. Before submitting a pull request, please make sure that your changes are well-tested and conform to the project's coding standards.
This project is licensed under the MIT License - see the LICENSE file for details.
This project was inspired by the Kaggle House Prices Competition and uses the Ames Housing dataset. Thanks to the Kaggle community for providing the dataset and inspiration for this project.