Python Feature Engineering Cookbook

How to do it...

Let's begin by importing the necessary tools and loading and preparing the data:

1. Import pandas and the required functions and classes from scikit-learn and Feature-engine:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.impute import SimpleImputerfrom feature_engine.missing_data_imputers import ArbitraryNumberImputer

2. Let's load the dataset:

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

3. 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)

Normally, we select arbitrary values that are bigger than the maximum value of the distribution.

4. Let's find the maximum value of ...