input: 
  import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import GradientBoostingClassifier
  
  def predict_winning_team(data: pd.DataFrame) -> float:
      """Develop a ML model in Python for predicting the winning team of football matches.
      Use data from the past 5 years of the Premier League.
      >>> data = pd.DataFrame({'home_team_goals': [1, 2, 3], 'away_team_goals': [1, 1, 1], 
      ...                      'home_team_possession': [50, 60, 70], 'away_team_possession': [50, 40, 30],
      ...                      'home_team_shots': [5, 6, 7], 'away_team_shots': [5, 4, 3],
      ...                      'home_team_yellow_cards': [1, 2, 0], 'away_team_yellow_cards': [0, 1, 2],
      ...                      'home_team_red_cards': [0, 0, 1], 'away_team_red_cards': [1, 0, 0],
      ...                      'winner': [0, 1, 1]})
      >>> predict_winning_team(data)
      0.5
      """
      # 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)
  
      # Evaluate the model
      score = gbm.score(X_test, y_test)
      
      return score
  
output:     
      # Predict the winning team for the test set
      predictions = gbm.predict(X_test)
      
      # Calculate the accuracy of the predictions
      accuracy = np.mean(predictions == y_test)
      
      return accuracy
  