input: from typing import List
  
  
  def predict_winner(data_path: str) -> float:
      """Predicts the winning team of football matches using a Gradient Boosting Classifier.
      The model uses data from the past 5 years of the Premier League and evaluates its 
      performance on a test set.
      
      >>> predict_winner('data.csv')
      Model score: 0.75
      """
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
      df = pd.read_csv(data_path)
  
      # Assuming the target column is 'winner' and other columns are features
      X = df.drop('winner', axis=1)
      y = df['winner']
  
      # 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()
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the accuracy score
      score = accuracy_score(y_test, y_pred)
  
      # Print the model score
      print(f"Model score: {score:.2f}")
  
      return score