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, split the data into training and test sets, train a GradientBoostingClassifier, make predictions, and evaluate the model. If the dataset is empty, the function should return a message indicating that there is no data to process.
  
  assert football_match_predictor() == 'Model score: 0.85'
  assert football_match_predictor() == 'No data to process.'
  assert football_match_predictor() == 'Model score: 0.78'
output: def football_match_predictor():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the data
      data = pd.read_csv('premier_league_data.csv')
  
      # Check if the dataset is empty
      if data.empty:
          return 'No data to process.'
  
      # Select relevant features and target variable
      features = data.drop('winning_team', axis=1)
      target = data['winning_team']
  
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      # Train a GradientBoostingClassifier
      model = GradientBoostingClassifier()
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Evaluate the model
      score = accuracy_score(y_test, y_pred)
  
      return f'Model score: {score:.2f}'