Feature engineering is the art of creating new features either from existing data in the dataset or by procuring additional data from an external data source. It is done with the intent that adding additional features improves the model performance. Feature engineering generally requires domain expertise and in-depth business problem understanding.

Let's take a look at an example of feature engineering—for a bank that is working on a loan defaulter prediction project, sourcing and supplementing the training dataset with information on the unemployment trends of the region for the past few months might improve the performance of the model.