Mastering categorical variables: Techniques and best practices for predictive modeling
Introduction: Handling categorical variables in predictive modeling
Categorical variables play a crucial role in many real-world datasets, as they represent non-numeric information such as categories, labels, or groups. However, most predictive modeling algorithms require numerical inputs, making it essential to pre-process categorical variables effectively. This comprehensive guide will explore various techniques for handling categorical variables in predictive modeling, their advantages and disadvantages, and best practices for using them.
1. Types of categorical variables
Categorical variables can be broadly classified into two types:
1.1 Nominal variables
Nominal variables represent categories that do not have any inherent order or ranking. Examples include colors, genders, or types of cuisine.
1.2 Ordinal variables
Ordinal variables represent categories with a natural order or ranking, such as education level, age group, or satisfaction ratings.
