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. Use the following features: home_team_goals, away_team_goals, home_team_possession, away_team_possession, home_team_shots, away_team_shots, home_team_yellow_cards, away_team_yellow_cards, home_team_red_cards, and away_team_red_cards. The target variable is 'winner'. Split the data into training and test sets, train a GradientBoostingClassifier, make predictions, and evaluate the model by printing its score.
  assert predict_winner("data.csv")==0.75
  assert predict_winner("data_2.csv")==0.70
  assert predict_winner("data_3.csv")==0.80
  assert predict_winner("")=="Data file not found"
output: def predict_winner(file_path):
      if not file_path:
          return "Data file not found"
      
      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 = ['home_team_goals', 'away_team_goals', 'home_team_possession', 'away_team_possession', 
                  'home_team_shots', 'away_team_shots', 'home_team_yellow_cards', 'away_team_yellow_cards', 
                  'home_team_red_cards', 'away_team_red_cards']
      X = data[features]
      y = data['winner']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = GradientBoostingClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      score = accuracy_score(y_test, y_pred)
      
      return score