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 GradientBoostingClassifier model, make predictions, and evaluate the model's performance. If the model score is successfully calculated, it should print the score. If the data is not available or any error occurs, it should handle the exception and print an error message.
  
  assert develop_football_model("data.csv") == None
  assert develop_football_model("nonexistent_file.csv") == "Data file not found"
  assert develop_football_model("") == "No file path provided"
output: def develop_football_model(file_path):
      try:
          if not file_path:
              print("No file path provided")
              return
          
          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(file_path)
          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)
          print(score)
      
      except FileNotFoundError:
          print("Data file not found")
      except Exception as e:
          print(f"An error occurred: {e}")