Encoding Categorical Data: A Step-by-Step Guide

Author(s): Souradip Pal

Originally published on Towards AI.

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Imagine you’re baking a cake, but instead of sugar, flour, and eggs, you have words like “vanilla,” “chocolate,” and “strawberry” on your countertop. As much as you’d like to start, there’s a problem — your recipe can only follow numeric measurements, not words. This is exactly what happens when you try to feed categorical data into a machine-learning model. The model needs numbers to work its magic, not strings of text.

In this hands-on tutorial, we’ll unravel the mystery of encoding categorical data so your models can process it with ease. We’ll break down the types of categorical data, discuss when and why each encoding method is used, and dive into Python code examples that show exactly how to get the job done.

Before we start transforming data, let’s get our definitions straight. In the world of data, you generally have two types: numerical and categorical. Machine learning models can easily understand numbers — no surprise there! But when it comes to words or labels, we need to convert these into numbers to help our models “understand” the data.

Ordinal Data:Ordinal data is like your favorite Netflix ranking list… Read the full blog for free on Medium.

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Published via Towards AI