input: Write a function to develop a machine learning model for predicting the winning team of football matches using data from the past 5 years of the Premier League. The function should load the dataset, select relevant features, split the data into training and test sets, train a Gradient Boosting Classifier model, make predictions, and evaluate the model's accuracy. The function should return the accuracy score of the model.
  assert develop_football_model() == 0.85
  assert develop_football_model() == 0.88
  assert develop_football_model() == 0.79
  assert develop_football_model() == 0.82
  assert develop_football_model() == 0.75
output: def develop_football_model():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.metrics import accuracy_score
      
      # Load dataset
      data = pd.read_csv('premier_league_data.csv')
      
      # Select relevant features
      features = ['home_team_strength', 'away_team_strength', 'home_team_form', 'away_team_form', 'home_team_injuries', 'away_team_injuries']
      X = data[features]
      y = data['winning_team']
      
      # Split data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Train Gradient Boosting Classifier model
      model = GradientBoostingClassifier(random_state=42)
      model.fit(X_train, y_train)
      
      # Make predictions
      y_pred = model.predict(X_test)
      
      # Evaluate model's accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy