Handling Categorical Features
Handling categorical features involves representing and processing information that isn’t inherently numerical. Categorical features are attributes that can take on a limited, fixed number of values or categories, and they often define distinct categories or groups within a dataset, such as types of products, genres of books, or customer segments. Effectively managing categorical data is crucial because most machine learning (ML) algorithms require numerical inputs.
In this chapter, we will cover the following topics:
- Label encoding
- One-hot encoding
- Target encoding (mean encoding)
- Frequency encoding
- Binary encoding
