input: 
  import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import GradientBoostingClassifier
  
  def predict_football_winner(data_path: str) -> 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.
      
      >>> predict_football_winner('data.csv')
      0.65
      """
      # Load data
      data = pd.read_csv(data_path)
  
      # 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)
  
      # Make predictions
      y_pred = gbm.predict(X_test)
  
      # Evaluate the model
      score = gbm.score(X_test, y_test)
      return score
  
output:     # Return the accuracy score
      return score