In machine learning, feature engineering is a blanket term covering both statistical and business judgment aspects of modeling real-world problems. Feature engineering is a new term coined recently to give due importance to the domain knowledge required to select sets of features for machine learning algorithms. It is one of the reasons that most of the machine learning professionals call it an informal process. In this chapter, we will provide an easy-to-use guide of key terms and methodology used in feature engineering. The chapter will give due weight to the domain knowledge and some common business limitations while using machine learning algorithms to solve business problems.