def METHOD_NAME(y_name="price", train_fraction=0.7, seed=None):
  """Get the imports85 data set.
  A description of the data is available at:
    https://archive.ics.uci.edu/ml/datasets/automobile
  The data itself can be found at:
    https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data
  Args:
    y_name: the column to return as the label.
    train_fraction: the fraction of the dataset to use for training.
    seed: The random seed to use when shuffling the data. `None` generates a
      unique shuffle every run.
  Returns:
    a pair of pairs where the first pair is the training data, and the second
    is the test data:
    `(x_train, y_train), (x_test, y_test) = get_imports85_dataset(...)`
    `x` contains a pandas DataFrame of features, while `y` contains the label
    array.
  """
  # Load the raw data columns.
  data = raw_dataframe()
  # Delete rows with unknowns
  data = data.dropna()
  # Shuffle the data
  np.random.seed(seed)
  # Split the data into train/test subsets.
  x_train = data.sample(frac=train_fraction, random_state=seed)
  x_test = data.drop(x_train.index)
  # Extract the label from the features dataframe.
  y_train = x_train.pop(y_name)
  y_test = x_test.pop(y_name)
  return (x_train, y_train), (x_test, y_test)