input: def predict_football_match_winner(data: pd.DataFrame) -> float:
      """Develop a machine learning model in Python to predict the winning team of football matches using past 5 years of Premier League data.
      The function loads the data, selects relevant features and the target variable, splits the data into training and test sets,
      trains a GradientBoostingClassifier, makes predictions on the test set, and returns the model's accuracy score.
      >>> data = pd.read_csv('test_data.csv')
      >>> score = predict_football_match_winner(data)
      >>> print(f'Model score: {score}')
      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
  
      # Select relevant features and target variable
      features = ['home_team_strength', 'away_team_strength', 'home_team_form', 'away_team_form', 'home_team_injuries', 'away_team_injuries']
      target = 'winner'
  
      X = data[features]
      y = data[target]
  
      # 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 a GradientBoostingClassifier
      model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Return the model's accuracy score
      return accuracy_score(y_test, y_pred)
  