Based on data from https://fbref.com I created model that predicts outcome of football game. Model is built on logistic regression with tuned hyperparameters.

• load_data.py - script to load data from website with scores. Takes 3 inputs (year_since (int), year_to (int), name(str) e.g. 15 19 test). Saves data in data folder,
• preprocess_data.py - script to create multiple features from data (e.g. # of home wins, # of home goals etc. All features are in model/model.yaml). Takes string as an input. Saves data in data folder,
• train_model.py - script to create and save model based on preprocessed data. Model parameteres are in model/model.yaml file. Takes string as an input. Saves model and transformer as joblib files in model folder,
• predict_scores.py - creates prediction based on preprocess data and created model. Takes string as an input. Saves predictions in predictions folder,

1. Load data ('python3 load_data.py 15 19 test'),
2. Preprocess data ('python3 preprocess_data.py test'),
3. Train model ('python3 train_model.py test'),
4. Predict scores ('python3 predict_scores.py test').

If model is trained you can load and preprocess data using get_predictions.py script ('python3 get_predictions.py 15 19 test').

For every game there are 3 possible outcomes:

• 1 - home team wins,
• -1 - away team wins,
• 0 - draw.

Predictions are made for soonest game week (or if week is in progress predictions ae only for games that are left in current game week).

Application is avalaible on https://adamiecscores.azurewebsites.net/