Python Feature Engineering Cookbook

How to do it...

Let's first import the necessary Python libraries and get the dataset ready:

1. Import the required Python libraries, functions, and classes:

import numpy as npimport pandas as pdfrom sklearn.model_selection import train_test_splitfrom feature_engine.categorical_encoders import RareLabelCategoricalEncoder

2. Let's load the dataset and divide it into train and test sets:

data = pd.read_csv('creditApprovalUCI.csv')X_train, X_test, y_train, y_test = train_test_split(    data.drop(labels=['A16'], axis=1), data['A16'],test_size=0.3,     random_state=0)

3. Let's determine the percentage of observations per category in the A7 variable:

X_train['A7'].value_counts() / len(X_train)

We can see the percentage of observations per category of