The method for selecting and transforming unprocessed data into a set of pertinent features that can be used in machine learning models is known as "feature engineering." Some of the common techniques of feature engineering include feature scaling, feature extraction, polynomial features, feature selection, text processing, and time series feature engineering. Overall, the choice of feature engineering techniques will depend on the specific problem and dataset being analyzed.
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