The scope of this project is to predict the outcome of an English Premier Leauge match, based on historic data.

For more information about the data used, check notes.txt.

Requirements:

• Poetry
• Python 3.10+
• pip install virtualenv

Create a virutal environment:

virtualenv .venv

Activate the environment:

source .venv/bin/activate

Install dependencies:

poetry install

Setup involves a 2 stage process.

• Run the Flask app
• Run training script and save the trained model.

Note: These steps are independent of each other as there is already a pre-trained model saved in AdaBoostClassifier.pkl

To run the application simply run the following command:

python __init__.py

The server should hopefully start running in http://127.0.0.1:5000

To train the model, simply run the following command:

python -m models.epl_engine

The default model used is AdaBoostClassifier, however you can change this to which ever sklearn model you desire by updating the main function in epl_engine.py

def main() -> None:
    # Change to the sklearn model you wish to use.
    from sklearn.ensemble import AdaBoostClassifier
    
    # ...code snippet

    # Update the following line accordingly to the imported model.
    clf = AdaBoostClassifier(n_estimators=500, learning_rate=1e-2)

Note: If you decide to change the model, be also sure to update the loaded classifier in the controller.py

# Change "AdaBoostClassifier.pkl" to whichever model you which to used
# saved in "models/trained/" folder.
CLF_PATH: str = os.path.join('models/trained/', 'AdaBoostClassifier.pkl')

• Victor I. Afolabi - victor-iyi
• Tomiiide - tomiiide

You are very welcome to modify and use them in your own projects.

Please keep a link to the original repository. If you have made a fork with substantial modifications that you feel may be useful, then please open a new issue on GitHub with a link and short description.

This project is opened under the MIT which allows very broad use for both private and commercial purposes.

A few of the images used for demonstration purposes may be under copyright. These images are included under the "fair usage" laws.