The categorical variables are firstly encoded as ordinal, then each integer value is represented as a binary vector that is all zero values except the index of the integer, which is marked with a 1. The goal of one-hot encoding is to transform data from a categorical representation to a numeric representation. It is an essential preprocessing step for many machine learning tasks. One-hot encoding is a process whereby categorical variables are converted into a form that can be provided as an input to machine learning models. Pandas get_dummies API for one-hot encoding.ColumnTransformer & OneHotEncoder for Multiple Categorical Features.OneHotEncoder for Single Categorical Feature.
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