Python Feature Engineering Cookbook

How to do it...

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

1. Import pandas and the required functions and classes:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom feature_engine.categorical_encoders import MeanCategoricalEncoder

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, data['A16'], test_size=0.3, random_state=0)

Note that, to encode with integers based on the target with pandas, we need to keep the target in the X_train and X_test datasets.

3. Let's determine the mean target value per category of the A7 variable and then store them in a dictionary:

ordered_labels ...