Categorical variables are typically strings, and pandas identifies them as object types. These 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.