Capturing missing values in a bespoke category

Missing data in categorical variables can be treated as a different category, so it is common to replace missing values with the Missing string. In this recipe, we will learn how to do so using pandas, scikit-learn, and Feature-engine.

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 pd
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from feature_engine.missing_data_imputers import CategoricalVariableImputer

2. Let's load the dataset:

data = pd.read_csv('creditApprovalUCI...