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 data, select relevant features, split the data into training and test sets, train a Gradient Boosting Classifier, make predictions, and evaluate the model.
  assert develop_ml_model({
      'data.csv': pd.DataFrame({
          'home_team_goals': [1, 2, 0],
          'away_team_goals': [2, 1, 0],
          'home_team_possession': [50, 60, 40],
          'away_team_possession': [50, 40, 60],
          'home_team_shots': [5, 10, 3],
          'away_team_shots': [10, 5, 7],
          'home_team_yellow_cards': [1, 2, 0],
          'away_team_yellow_cards': [2, 1, 1],
          'home_team_red_cards': [0, 0, 0],
          'away_team_red_cards': [0, 0, 0],
          'winner': [0, 1, 2]
      })
  })['score'] >= 0.5
  
  assert develop_ml_model({
      'data.csv': pd.DataFrame({
          'home_team_goals': [1, 2, 0],
          'away_team_goals': [1, 1, 1],
          'home_team_possession': [50, 60, 40],
          'away_team_possession': [50, 40, 60],
          'home_team_shots': [5, 10, 3],
          'away_team_shots': [5, 5, 5],
          'home_team_yellow_cards': [1, 2, 0],
          'away_team_yellow_cards': [1, 1, 1],
          'home_team_red_cards': [0, 0, 0],
          'away_team_red_cards': [0, 0, 0],
          'winner': [0, 1, 2]
      })
  })['score'] >= 0.5
output: 
  def develop_ml_model(data_dict):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import GradientBoostingClassifier
      from sklearn.metrics import accuracy_score
  
      df = data_dict['data.csv']
      features = df.drop(columns=['winner'])
      target = df['winner']
  
      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)
      score = accuracy_score(y_test, y_pred)
  
      return {'score': score}
  