input: 
  from typing import List
  
  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': [2, 1, 3],
      ...     'away_team_goals': [1, 0, 2],
      ...     'home_team_possession': [60, 55, 58],
      ...     'away_team_possession': [40, 45, 42],
      ...     'home_team_shots': [8, 6, 9],
      ...     'away_team_shots': [3, 4, 5],
      ...     'home_team_yellow_cards': [1, 0, 2],
      ...     'away_team_yellow_cards': [2, 1, 3],
      ...     'home_team_red_cards': [0, 1, 0],
      ...     'away_team_red_cards': [0, 0, 1],
      ...     'winner': ['home', 'home', 'home']
      ... })
      >>> predict_winning_team(data)
      1.0
      """
      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)
      score = gbm.score(X_test, y_test)
      return score
  
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      import pandas as pd