“Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.” -Dr. Jason Brownlee
Examples:
Deriving a person’s age from birth date and the current date
Extracting word and phrase occurrence counts from text documents
Getting the average and median view count of specific songs and music videos
Extracting pixel information from raw images
Why Feature Engineering?
Better representation of the underlying raw data.
Better features make better models.
Essential for model building and evaluation.
Feature engineering is useful to build models on diverse data types.
Feature engineering emphasizes focusing on the business and domain of the problem when building features.