A categorical variable is a type of variable that can take on a limited number of distinct values, often referred to as categories or levels. For example, responses to a survey question about breakfast frequency might include options like “Never,” “Rarely,” “Most days,” or “Every day.” Similarly, a survey about car ownership could categorize responses into brands such as “Honda,” “Toyota,” and “Ford.” These variables are essential in data analysis as they help classify observations into meaningful groups ¹³.

When working with categorical variables in data analysis or machine learning, it’s crucial to preprocess them appropriately. Most machine learning models cannot directly handle categorical data, which may lead to errors if not addressed. Common approaches to manage categorical variables include dropping them from the dataset, encoding them into numerical formats, or using specialized data types like pandas’ categorical data type ⁴⁵.

Understanding how to handle categorical variables is vital for effective data analysis and model building.