Feature Engineering

A machine learning technique called feature engineering uses data to generate new variables that aren't present in the training set. The process of producing, transforming, extracting, and choosing features also referred to as variables that are most helpful in developing an accurate ML algorithm is known as feature engineering. With the aim of streamlining and accelerating data transformations, while also improving model accuracy, it can generate new features for both supervised and unsupervised learning. Designing precise prediction models to tackle issues while requiring less time and computational resources requires feature engineering.

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