code: def default(return_X_y=True):
      
      # y is binary
      # recommend LogisticGAM
      default = pd.read_csv(PATH + '/default.csv', index_col=0)
      if return_X_y:
          default = default.values
          default[:,0] = np.unique(default[:,0], return_inverse=True)[1]
          default[:,1] = np.unique(default[:,1], return_inverse=True)[1]
          X = default[:,1:]
          y = default[:,0]
          return _clean_X_y(X, y)
      return default
docs: credit default dataset
  
      Parameters
      ----------
      return_X_y : bool,
          if True, returns a model-ready tuple of data (X, y)
          otherwise, returns a Pandas DataFrame
  
      Returns
      -------
      model-ready tuple of data (X, y)
          OR
      Pandas DataFrame
  
      Notes
      -----
      X contains the category of student or not, credit card balance,
      and  income.
  
      y contains the outcome of default (0) or not (1).
  
      Source:
      https://vincentarelbundock.github.io/Rdatasets/doc/ISLR/Default.html