MACHINE LEARNING

Detailed explanation-1: -As categorical features, they take on levels or values. These can be represented as various categories such as age, state, or customer type for example. Alternatively, these can be created by binning underlying numeric features, such as identifying individuals by age ranges (e.g., 0–10, 11–18, 19–30, 30–50, etc.).

Detailed explanation-2: -A categorical variable is a variable that can be divided into one or more groups(categories). Age of a person is not categorical because age can be expressed on multiple numerical values. It cannot be expressed into categories.