A Data Scientist’s Toolkit to Encode Categorical Variables to Numeric

Encoding categorical variables into numeric formats is a crucial and frequent part of a data scientist’s work. Properly handling these variables ensures that machine learning models can effectively process and extract valuable insights from the data. I’d like to share some practical tips for those who need guidance in encoding categorical variables. These techniques are ones I frequently use in my professional projects, and they’ve consistently helped improve model performance by transforming raw categorical data into features that better capture relationships and patterns.

In this guide, I’ll cover several commonly used encoding techniques. Each method has its own strengths and ideal use cases, depending on the nature of the data and the problem you’re solving. By mastering these methods, you’ll be better equipped to tailor your feature engineering process and optimize your models. The techniques discussed are as follows:

• Dummy/One-Hot Encoding
• Mean Encoding (Target Encoding)
• Weight of Evidence (WoE)
• Leave-One-Out Encoding

Many Python libraries support encoding methods, making it easy to incorporate into your workflow. To illustate the process of a method, I will show you the manual code and the…