input: def predict_winning_team(data: pd.DataFrame) -> float:
      """ Develop a Machine Learning model using Gradient Boosting Classifier to predict
      the winning team of football matches based on features from the past 5 years of the
      Premier League. The function takes a pandas DataFrame as input and returns the accuracy
      score of the model.
      
      >>> data = pd.read_csv('data.csv')
      >>> predict_winning_team(data)
      0.78
      """
      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']]
      target = data['winner']
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
      gbm = GradientBoostingClassifier()
      gbm.fit(X_train, y_train)
      y_pred = gbm.predict(X_test)
      return gbm.score(X_test, y_test)
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier