Replacing categories with ordinal numbers

Ordinal encoding consists of replacing the categories with digits from 1 to k (or 0 to k-1, depending on the implementation), where k is the number of distinct categories of the variable. The numbers are assigned arbitrarily. Ordinal encoding is better suited for non-linear machine learning models, which can navigate through arbitrarily assigned numbers to find patterns that relate to the target.

In this recipe, we will perform ordinal encoding using pandas, scikit-learn, and feature-engine.

How to do it...

First, let’s make the import and prepare the dataset:

1. Import pandas and the data split function:

   import pandas as pd
   from sklearn.model_selection import train_test_split

2. Let’s load the Credit Approval dataset and divide it into train and test sets:

   data = pd.read_csv("credit_approval_uci.csv")
   X_train, X_test, y_train, y_test = train_test_split(
       data.drop(labels=["target...