Feature Engineering At a glance - DataScienceCentral.com

Data Science Lifecycle revolves around using various analytical methods to produce insights and applying Machine Learning techniques to do the predictions from the collected dataset. The main objective is to achieve a business challenge. The entire process involves several steps like data cleaning, preparation, modeling, model evaluation, etc. Depends on the nature of the data and problem statements, the % of the individual tasks might differ in the life cycle as shown in the above figure. In this Lifecycle, the Feature Engineering is very important and very sensitive for model build and evaluation. Let's discuss in detail Feature Engineering What is called Feature(s) in Data Science/Machine Learning?