input: 
  def predict_football_winner():
      """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 data includes features such as home team goals, away team 
      goals, possession, shots, and cards. The model is trained using Gradient Boosting Classifier and evaluated on the 
      test data.
      
      Example:
      >>> predict_football_winner()
      '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 dataset
      data = pd.read_csv('premier_league_data.csv')
  
      # Features and target variable
      features = ['home_team_goals', 'away_team_goals', 'possession', 'shots', 'cards']
      target = 'winner'
  
      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 Gradient Boosting Classifier
      model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Evaluate the model
      score = accuracy_score(y_test, y_pred)
  
      return f'Model score: {score:.2f}'