Encoding data using target encoding

As we know, machines are only capable of understanding numbers. However, plenty of real-world ML problems revolve around objects and information that are not necessarily numerical in nature. Things such as states, names, and classes, in general, are represented as categories rather than numbers. This kind of data is called categorical data. Categorical data will often play a big part in analysis and prediction. Hence, there is a need to convert these categorical values to a numerical format so that machines can understand them. The conversion should also be in such a way that we do not lose the inherent meaning of those categories, nor do we introduce new information into the data, such as the incremental nature of numbers, for example.

This is where encoding is used. Encoding is a process where categorical values are transformed, in other words, encoded, into numerical values. There are plenty of encoding methods that can perform this transformation...