input: Write a function to 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 function should load the data, select relevant features and target, split the data into training and test sets, train a Gradient Boosting Classifier, make predictions, and evaluate the model. The function should not return anything, but it should print the model's score.
  assert develop_ml_model_for_football_matches('data.csv') is None
  assert develop_ml_model_for_football_matches('data_empty.csv') is None
  assert develop_ml_model_for_football_matches('data_incomplete.csv') is None
output: def develop_ml_model_for_football_matches(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)
          if data.empty or data.isnull().all().any():
              print("Data is empty or incomplete.")
              return
          
          features = data.drop(columns=['winning_team'])
          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)
          print(f"Model score: {score:.2f}")
      
      except Exception as e:
          print(f"An error occurred: {e}")