input: Write a function to develop a ML model in Python for predicting the winning team of football matches using data from the past 5 years of the Premier League. The function should load the data, select relevant features and the target, split the data into training and test sets, train a GradientBoostingClassifier, make predictions, and evaluate the model's performance. The function should return the model's score.
  assert test_football_winner() == 0.75
  assert test_football_winner() == 0.82
  assert test_football_winner() == 0.78
  assert test_football_winner() == 0.76
  assert test_football_winner() == 0.74
output: def test_football_winner():
      data = pd.read_csv('premier_league_data.csv')
      features = ['home_team_strength', 'away_team_strength', 'home_team_form', 'away_team_form', 'home_team_injuries', 'away_team_injuries']
      target = 'winning_team'
      X = data[features]
      y = data[target]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model = GradientBoostingClassifier(random_state=42)
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)