input: 
  def predict_football_match_winner() -> float:
      """ This function trains a machine learning model to predict the winning team of football
      matches using data from the past 5 years of the Premier League. It splits the data into
      training and test sets, trains a GradientBoostingClassifier, and evaluates the model's score.
      >>> predict_football_match_winner()
      Model score: 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 dataset
      data = pd.read_csv('premier_league_data.csv')
  
      # Preprocess the data
      X = data.drop('winner', axis=1)
      y = data['winner']
  
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = GradientBoostingClassifier()
      model.fit(X_train, y_train)
  
      # Evaluate the model
      y_pred = model.predict(X_test)
      score = accuracy_score(y_test, y_pred)
  
      print(f"Model score: {score:.2f}")
      return score