Python Feature Engineering Cookbook

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 pdfrom sklearn.model_selection import train_test_splitfrom feature_engine.missing_data_imputers import RandomSampleImputer

2. Let's load the dataset:

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

3. The random values that will be used to replace missing data should be extracted from the train set, so 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)

First, we will run the commands line by line ...