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3.3 Encoding and Handling Categorical Data

In the realm of real-world datasets, categorical data is a common occurrence. These features represent distinct categories or labels, as opposed to continuous numerical values. The proper handling of categorical data is of paramount importance, as the vast majority of machine learning algorithms are designed to work with numerical inputs. Improper encoding of categorical variables can have severe consequences, potentially leading to suboptimal model performance or even causing errors during the training process.

This section delves into an array of techniques for encoding and managing categorical data. We will explore fundamental methods such as one-hot encoding and label encoding, as well as more nuanced ...