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 the arbitrarily assigned digits to try and 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...

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

1. Import pandas and the required function and classes:

import pandas as pd
from sklearn...