input: def predict_winning_team(data: pd.DataFrame) -> float:
      """ Develop a Machine Learning model in Python for predicting the winning team of football matches using data from the past 5 years of the Premier League.
      The model uses features such as goals scored, possession percentage, shots on target, and cards received by each team.
      The output is the accuracy score of the model.
      >>> data = pd.read_csv('data.csv')
      >>> predict_winning_team(data)
      0.75
      """
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Prepare the features and target
      X = data[['goals_scored', 'possession_percentage', 'shots_on_target', 'cards_received']]
      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 model
      model = RandomForestClassifier(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