The process of transforming raw datasets (post clean up and wrangling) into features that can be utilized by ML algorithms is a combination of domain knowledge, use case requirements, and specific techniques. Features thus depict various representations of the underlying data and are the outcome of the feature engineering process.

Since feature engineering transforms raw data into a useful representation of itself, there are various standard techniques and strategies that can be utilized, based on the type of data at hand. In this section we will discuss a few of those strategies, briefly covering both structured and unstructured data.