Implementing random sample imputation

Random sampling imputation consists of extracting random observations from the pool of available values in the variable. Random sampling imputation preserves the original distribution, which differs from the other imputation techniques we've discussed in this chapter and is suitable for numerical and categorical variables alike. In this recipe, we will implement random sample imputation with pandas and Feature-engine.

How to do it...

Let's begin by importing the required libraries and tools and preparing the dataset:

1. Let's import pandas, the train_test_split function from scikit-learn, and RandomSampleImputer from Feature-engine:

import pandas as pd
from...