Encoding categorical data is the process of turning categorical data into integer format so that converted categorical values can be delivered to various models.
This means that if your data contains categorical information, you must convert it to numbers before fitting and evaluating a model. Although a newer technique called learnt embedding may provide a valuable middle ground between these two ways, the two most used techniques are integer encoding and one hot encoding.
One of the benefits of encoding categorical data is that it helps in avoiding missing values.
Another benefit of encoding categorical data is that it helps in reducing the number of features and making the model simpler.
Encoding categorical data is the process of turning categorical data into integer format so that the data with converted categorical values can be delivered to models for prediction and improvement.
Encoding is a method of transforming category variables into numerical values, which may then be easily fitted to a machine learning model.
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