Categorical Variables

Categorical variables organize observations according to predefined labels or characteristics. They are generally divided into nominal variables, where categories have no natural order, and ordinal variables, where categories follow a meaningful order. Because many machine learning algorithms require numerical inputs, categorical data is often transformed using techniques such as one-hot encoding or label encoding before model training.

Categorical variables allow machine learning models to incorporate important non-numerical characteristics into their predictions. Handling them correctly helps preserve meaningful information, improve model performance, and prevent misleading relationships from being introduced during data preprocessing.