input: Write a function to predict the winning team of football matches using a machine learning model trained on 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.
  assert predict_winner('data.csv')=='Model score: 0.85'
  assert predict_winner('new_data.csv')=='Model score: 0.87'
  assert predict_winner('empty_data.csv')=='Error: No data available'
output: def predict_winner(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:
              return 'Error: No data available'
          
          features = data.drop('winning_team', axis=1)
          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()
          model.fit(X_train, y_train)
          
          y_pred = model.predict(X_test)
          score = accuracy_score(y_test, y_pred)
          
          return f'Model score: {score:.2f}'
      except Exception as e:
          return f'Error: {str(e)}'