code: def default_preprocessing(df):
      # Details and preprocessing for FICO dataset
  
      # minimize dependence on ordering of columns in heloc data
      # x_cols, y_col = df.columns[0:-1], df.columns[-1]
      x_cols = list(df.columns.values)
      x_cols.remove("RiskPerformance")
      y_col = list(["RiskPerformance"])
  
      # Preprocessing the HELOC dataset
      # Remove all the rows containing -9 in the ExternalRiskEstimate column
      # df = df[df.ExternalRiskEstimate != -9]
      # add columns for -7 and -8 in the dataset
      for col in x_cols:
          df[col][df[col].isin([-7, -8, -9])] = 0
      # Get the column names for the covariates and the dependent variable
      df = df[(df[x_cols].T != 0).any()]
  
      # minimize dependence on ordering of columns in heloc data
      # x = df.values[:, 0:-1]
      x = df[x_cols].values
  
      # encode target variable ('bad', 'good')
      cat_values = df[y_col].values
      enc = LabelEncoder()
      enc.fit(cat_values)
      num_values = enc.transform(cat_values)
      y = np.array(num_values)
  
      return np.hstack((x, y.reshape(y.shape[0], 1)))
  
file_path: AIX360/aix360/datasets/heloc_dataset.py
imports: import os
  import pandas
  import numpy
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 2 for calling a global function with example usage: default_preprocessing(df) and returns: np