input: Write a function to develop an 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 relevant features and target, split the data into training and test sets, train a GradientBoostingClassifier, make predictions, and evaluate the model's performance.
  
  assert test_football_prediction_model()==None
  assert print('Model score:', test_football_prediction_model()) in ['Model score: 0.85', 'Model score: 0.78', 'Model score: 0.92']
output: def test_football_prediction_model():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.metrics import accuracy_score
  
      data = pd.read_csv('premier_league_data.csv')
      features = data[['home_team_strength', 'away_team_strength', 'home_team_form', 'away_team_form']]
      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(random_state=42)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      score = accuracy_score(y_test, y_pred)
  
      return score