Technical requirements

In this chapter, we will use the Matplotlib, pandas, NumPy, scikit-learn, feature-engine, and Category Encoders Python libraries. If you need to install Python, the free Anaconda Python distribution (https://www.anaconda.com/) includes most numerical computing libraries.

feature-engine can be installed with pip:

pip install feature-engine

If you use Anaconda, you can install feature-engine with conda:

conda install -c conda-forge feature_engine

To install Category Encoders, use pip as follows:

pip install category_encoders

We will use the Credit Approval dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/), licensed under the CC BY 4.0 creative commons attribution: https://creativecommons.org/licenses/by/4.0/legalcode. You’ll find the dataset at this link: http://archive.ics.uci.edu/dataset/27/credit+approval.

I downloaded and modified the data as shown in this notebook: https://github.com/PacktPublishing...