Categorical variables are typically represented as strings, and pandas identifies them as object types. However, some variables that appear to be numerical may actually be categorical (e.g., the number of doors a car has). All these categorical variables need to be converted to a numerical form because ML models can interpret only numerical features. It is possible to incorporate certain categories from a feature, not necessarily all of them. This transformation from categorical to numerical variables is known as One-Hot encoding.
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This way of encoding categorical features is called "one-hot encoding". We'll learn more about it in Session 3.