The Ultimate Guide to Encoding Categorical Data for Machine Learning

Learn the best encoding techniques for categorical data—Label, One-Hot, Ordinal & Frequency Encoding—to boost machine learning accuracy and avoid common pitfalls.

Machine learning models work best with numbers, but most real-world data aren’t always this simple. A lot of datasets include categories—things like colors, brands, or customer types—that don’t come as numbers.

If we don’t handle these properly, they can seriously hurt our model’s accuracy.

In this article, I’ll go over different ways to turn categorical data into numbers, explain when to use each method, and walk through a real-world retail dataset to show these techniques in action. Along the way, we’ll also cover common mistakes, like data leakage, so you know what to avoid.

I’ll cover the four different types of encoding and really break them down so you can visualize when and why we should use one over the other. You’ll learn about Label, One-Hot, Ordinal, and Frequency Encoding in this read today.

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By the end, you’ll understand how to handle categorical data the right way—an important skill for anyone working with machine learning.

So, how do we turn categories into numbers that machine learning models can understand? The solution to this problem is known as encoding, which converts non-numeric labels into a format models can process.

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Alright, time to jump straight into some encoding Python crew! Hope you’ve all had an amazing week so far!