Feature Engineering

Feature engineering refers to the process of constructing explanatory variables from datasets that can be used to train machine learning models to solve prediction problems. Typically, data is scattered across multiple tables and needs to be consolidated into a single table with rows representing observations and columns representing features. The goal of feature engineering is to enhance the predictive performance and generalization ability of the model by extracting, transforming, and selecting relevant features, thereby creating greater value in practical applications.