Most data scientists and statisticians agree that predictive modeling is both art and science yet, relatively little to no air time is given to describing the art. This post describes one piece of the art of modeling called feature engineering which expands the number of variables you have to build a model. I offer six ways to implement feature engineering and provide examples of each. Using methods like these is important because additional relevant variables increase model accuracy, which makes feature engineering an essential part of the modeling process. The full white paper may be downloaded at Feature Engineering Tips for Data Scientists.