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 pandas as pdimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom feature_engine.categorical_encoders import OrdinalCategoricalEncoder

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.

To better understand the monotonic relationship concept, let's plot the relationship ...