Marking imputed values

A missing indicator is a binary variable that takes the value 1 or True to indicate whether a value was missing, or 0 or False otherwise. It is common practice to replace missing observations with the mean, median, or most frequent category while simultaneously marking those missing observations with missing indicators. In this recipe, we will learn how to add missing indicators using pandas, scikit-learn, and feature-engine.

How to do it...

Let’s begin by making some imports and loading the data:

1. Let’s import the required libraries, functions, and classes:

   import pandas as pd
   import numpy as np
   from sklearn.model_selection import train_test_split
   from sklearn.impute import SimpleImputer
   from sklearn.compose import ColumnTransformer
   from sklearn.pipeline import Pipeline
   from feature_engine.imputation import(
       AddMissingIndicator, CategoricalImputer,
       MeanMedianImputer
      ...