Many datasets contain categorical features, representing information like product types, geographical locations, or user groups. These features often hold valuable predictive information, but most machine learning algorithms require numerical input. This chapter focuses on bridging this gap by introducing methods to convert categorical data into suitable numerical representations.

You will learn to:

• Distinguish between nominal and ordinal categorical data and understand why this difference matters for encoding.
• Implement common encoding strategies such as One-Hot Encoding, Ordinal Encoding, Target Encoding, Binary Encoding, and Hashing.
• Address the specific challenge of encoding features with a large number of unique categories (high cardinality).
• Compare the trade-offs associated with different encoding techniques.

We will use Python libraries like Pandas and Scikit-learn to apply these techniques, preparing your categorical data for effective use in machine learning models.