input: Write a function to develop a ML model in Python that predicts the winning team of football matches using data from the past 5 years of the Premier League. The function should load the data, select features, split the data into training and test sets, train the model using a GradientBoostingClassifier, make predictions on the test set, and evaluate the model. If the data file is not available, the function should return a message indicating that the data is missing.
  assert predict_football_winner('data.csv') == 'Model score: [a number between 0 and 1]'
  assert predict_football_winner('nonexistent_file.csv') == 'Data file not found.'
  assert predict_football_winner('') == 'Data file not found.'
output: def predict_football_winner(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.metrics import accuracy_score
  
      try:
          data = pd.read_csv(file_path)
      except FileNotFoundError:
          return 'Data file not found.'
  
      features = data.drop('winning_team', axis=1)
      target = data['winning_team']
  
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      model = GradientBoostingClassifier()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      score = accuracy_score(y_test, y_pred)
  
      return f'Model score: {score}'