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 features, split the data into training and test sets, train a GradientBoostingClassifier and print the model score. If the given CSV file is empty, the function should return a message "No data available".
  assert football_match_predictor() == "Model score: 0.75"  # Assuming this score is based on average historical performance of the model when trained with the given features and data.
  assert football_match_predictor('empty_file.csv') == "No data available"
  assert football_match_predictor() is None  # Make sure it doesn't return anything else when executed successfully.
output: def football_match_predictor(csv_file='premier_league_data.csv'):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
  
      try:
          df = pd.read_csv(csv_file)
          if df.empty:
              return "No data available"
          
          features = df.drop(columns=['winning_team'])
          target = df['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)
          
          score = model.score(X_test, y_test)
          print(f"Model score: {score:.2f}")
      
      except FileNotFoundError:
          return "No data available"