Python Feature Engineering Cookbook

How to do it...

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

1. Import pandas and the required function and classes:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import OrdinalEncoderfrom 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.drop(labels=['A16'], axis=1), data['A16'],test_size=0.3,         random_state=0)

3. Let's encode the A7 variable for this demonstration. First, let's make a dictionary of category to integer pairs and then display the result:

ordinal_mapping = {k: i for ...