input: def predict_football_winner(data: pd.DataFrame) -> float:
      """
      Develop a ML model using Gradient Boosting Classifier to predict the winning team of football matches. The data should include features such as goals, possession, shots, and cards for both home and away teams. The function returns the accuracy score of the model.
      >>> data = pd.read_csv('data.csv')
      >>> score = predict_football_winner(data)
      >>> print(f'Model score: {score}')
      Model score: 0.75
      """
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.metrics import accuracy_score
  
      # Prepare the data
      X = data.drop('winning_team', axis=1)
      y = data['winning_team']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Initialize the Gradient Boosting Classifier
      model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Calculate the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy