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 features, split the data into training and test sets, train a Gradient Boosting Classifier model, make predictions, and evaluate the model's score. If the data is loaded successfully and the model is trained, return the model's score as a float. If the data is not loaded or there is any error during the process, return -1.
  assert develop_ml_model('data.csv') > 0.7
  assert develop_ml_model('non_existent_file.csv') == -1
output: def develop_ml_model(file_path):
      try:
          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)
          
          return score
      except:
          return -1