How to do it...

To proceed with the recipe, let's import the required tools and prepare the dataset:

  1. Import pandas and the required functions and classes from scikit-learn and Feature-engine:
import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.impute import SimpleImputerfrom feature_engine.missing_data_imputers import CategoricalVariableImputer
  1. Let's load the dataset:
data = pd.read_csv('creditApprovalUCI.csv')
  1. Let's separate the data into train and test sets:
X_train, X_test, y_train, y_test = train_test_split(    data.drop('A16', axis=1), data['A16'], test_size=0.3,     random_state=0)
  1. Let's replace missing values in four categorical variables by using the Missing string:
for var in ['A4', 'A5', 'A6', 'A7']: ...

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