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, train a Gradient Boosting Classifier, make predictions, and evaluate the model.
  assert predict_winner("data.csv")==0.75
  assert predict_winner("data_2.csv")==0.82
  assert predict_winner("data_3.csv")==0.68
  assert predict_winner("data_4.csv")==0.79
  assert predict_winner("")==None
output: def predict_winner(file_path):
      if not file_path:
          return None
      
      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(columns=['winning_team'])
      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(random_state=42)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy