input: 
  def predict_football_winner(data_file: str) -> float:
      """
      Predicts the winning team of football matches using a Gradient Boosting Classifier
      based on features from a given dataset. The dataset should contain features such as
      goals, possession, shots, and cards for both home and away teams, as well as the
      target 'winner' which indicates the winning team.
  
      >>> predict_football_winner('data.csv')
      0.75
      """
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
  
      # Load data
      data = pd.read_csv(data_file)
  
      # 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:     # Return the accuracy score of the model
      return score