input: def predict_football_winner(data: pd.DataFrame) -> float:
      """ Predicts the winning team of football matches using a GradientBoostingClassifier. 
      The input data should include features such as home and away team goals, possessions, shots,
      and yellow/red cards. The function returns the accuracy score of the model on the test set.
      
      >>> df = pd.read_csv('data.csv')
      >>> score = predict_football_winner(df)
      >>> print(f'Model score: {score}')
      Model score: 0.74
      """
      
      # Select features
      features = data[['home_team_goals', 'away_team_goals', 'home_team_possession', 'away_team_possession',
                       'home_team_shots', 'away_team_shots', 'home_team_yellow_cards', 'away_team_yellow_cards',
                       'home_team_red_cards', 'away_team_red_cards']]
      
      # Select target
      target = data['winner']
      
      # Split data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
      
      # Train the model
      gbm = GradientBoostingClassifier()
      gbm.fit(X_train, y_train)
      
      # Make predictions
      y_pred = gbm.predict(X_test)
      
      # Evaluate the model
      score = gbm.score(X_test, y_test)
      return score
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier