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 One hot encoding approach is used to encode category data as numerical variables. It is also known as "dummy encoding" or "one-of-K encoding." The procedure entails establishing a new binary variable for each category in the categorical variable. This can be beneficial in machine learning and data analysis when working with categorical variables that do not have a natural order or ranking. When is it appropriate to execute one hot encoding? One hot encoding is appropriate for usage when the categorical variable is not ordinal, which means the categories do not have a natural order or ranking. It is also beneficial when the category variable has numerous levels or categories. For example, a variable with the levels "red", "green", and "blue" would be a good candidate for one hot encoding. One popular top category encoding When working with huge datasets, encoding all levels of a category variable with a single hot might result in a sign