planetbridging / afl_auto_predict

AFL Match Prediction

This project is a Flask-based web application that predicts Australian Football League (AFL) match outcomes using machine learning. It fetches current AFL match data, processes historical match data, and uses a TensorFlow model to make predictions.

Features

• Fetches and converts AFL historical data from Excel to CSV format
• Retrieves current AFL match schedules
• Predicts match outcomes using a TensorFlow LSTM model
• Displays predictions in a web interface
• Dockerized for easy deployment and consistency across environments

Prerequisites

To run this project, you need to have the following installed:

• Docker
• Docker Compose

Setup and Installation

1. Clone the repository:

   git clone https://github.com/planetbridging/afl-prediction.git
   cd afl-prediction
   

2. Build and run the Docker container:

   docker-compose up --build
   

3. Access the application by navigating to http://localhost:5008/afl in your web browser.

Project Structure

• app.py: Main Flask application file
• afl_models.py: Contains the TensorFlow model for match prediction
• excel_convert.py: Handles conversion of Excel data to CSV
• get_current_matches.py: Fetches current AFL match data
• templates/afl.html: HTML template for displaying predictions
• Dockerfile: Defines the Docker image for the application
• docker-compose.yml: Defines the services for Docker Compose
• requirements.txt: Lists Python package dependencies

How It Works

1. The application fetches historical AFL data and converts it to CSV format.
2. Current AFL match schedules are retrieved from a web source.
3. For each upcoming match, the application uses a TensorFlow LSTM model to predict the outcome based on historical data.
4. Predictions are displayed in a web interface, showing the likely winner and predicted scores.

Customization

You can modify the TensorFlow model in afl_models.py to experiment with different architectures or hyperparameters. Adjust the data fetching and processing in excel_convert.py and get_current_matches.py if the data sources or formats change.

Contributing

Contributions to improve the project are welcome. Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/AmazingFeature)
3. Make your changes
4. Commit your changes (git commit -m 'Add some AmazingFeature')
5. Push to the branch (git push origin feature/AmazingFeature)
6. Open a Pull Request

License

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

Acknowledgments

• Data provided by Austadiums and AusSportsBetting
• Inspired by the exciting world of AFL and the power of machine learning in sports prediction