input: def football_match_predictor(data_path: str) -> float:
      """
      Develop a ML model in Python for predicting the winning team of football matches. 
      Uses data from the past 5 years of the Premier League to train a GradientBoostingClassifier.
      The model is trained on features such as goals, possession, shots, and cards of both home and away teams.
      Returns the accuracy score of the model on the test data.
  
      >>> football_match_predictor('data.csv')
      0.85
      """
output:     import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the data
      data = pd.read_csv(data_path)
  
      # Define features and target
      features = ['home_goals', 'home_possession', 'home_shots', 'home_cards', 'away_goals', 'away_possession', 'away_shots', 'away_cards']
      target = 'winning_team'
  
      X = data[features]
      y = data[target]
  
      # 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 = GradientBoostingClassifier(random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy