input: Write a python function to split a dataset into training and testing sets using the train_test_split method from sklearn.
  assert len(split_dataset(data, 'position', 0.20, 1)[0]) == 0.8 * len(data)
  assert len(split_dataset(data, 'position', 0.20, 1)[1]) == 0.2 * len(data)
  assert len(split_dataset(data, 'position', 0.30, 42)[0]) == 0.7 * len(data)
  assert len(split_dataset(data, 'position', 0.30, 42)[1]) == 0.3 * len(data)
output: from sklearn.model_selection import train_test_split
  
  def split_dataset(data, target_column, test_size, random_state):
      X = data.drop(columns=[target_column])
      y = data[target_column]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
      return (X_train, X_test, y_train, y_test)