input: def predict_football_match_winner(data: pd.DataFrame) -> np.ndarray:
      """ This function develops a machine learning model to predict the winning team of football matches
      using data from the past 5 years of the Premier League. The data should include features such as 
      home and away team goals, possession, shots, and cards. The output is the predicted winners based 
      on the test data.
  
      >>> data = pd.read_csv('data.csv')
      >>> predictions = predict_football_match_winner(data)
      >>> print(predictions[:5])
      [1 2 2 1 2]
      """
output:     from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Prepare features and target
      X = data.drop(columns=['winner'])
      y = data['winner']
  
      # Split data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train a RandomForestClassifier
      model = RandomForestClassifier(n_estimators=100, random_state=42)
      model.fit(X_train, y_train)
  
      # Predict the winners
      predictions = model.predict(X_test)
  
      return predictions