Python Feature Engineering Cookbook

How to do it...

Let's begin by importing the necessary Python libraries and preparing the data:

1. Let's import pandas and the required function and class from scikit-learn, and the missing data imputation module from Feature-engine:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.pipeline import Pipelineimport feature_engine.missing_data_imputers as mdi

2. Let's load the dataset:

data = pd.read_csv('creditApprovalUCI.csv')

3. Let's divide 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)

4. Let's create lists with the names of the variables that we want to apply specific imputation techniques to: