This project focuses on predicting the market values of football players using machine learning techniques. By analyzing various player performance statistics such as age, goals, dribbling, and reputation, the system provides estimates of their market worth. The project includes both server-side and client-side implementations, with a Flask-based RESTful API for the server and web and mobile interfaces for the client.
- Predicts market values of football players based on performance stats.
- Provides a user-friendly web interface for inputting player statistics and viewing predictions.
- Offers a mobile app for convenient access to predictions on-the-go.
- Utilizes machine learning algorithms, including random forest regression, for accurate predictions.
- Implements data visualization techniques, such as correlation heatmaps, for insights into the predictive factors.
- Python
- Flask
- Streamlit
- Flutter
- HTTP
- JSON
- React-Vite
- Machine Learning (Random Forest Regression)
- Data Visualization
- Clone the repository to your local machine.
- Set up the Flask server by navigating to the
appdirectory and runningpython app.py. - Access the web interface by opening the provided URL in your browser.
- Use the mobile app by installing it on your device and launching it.
Contributions to this project are welcome. If you have suggestions for improvements or would like to report a bug, please open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
- Special thanks to the contributors and open-source projects that helped make this project possible.
For any inquiries or support, please contact on Linkedin.