Feature engineering? Start here!

One of the hot topics on Machine Learning is, with no doubts, feature engineering. In fact, it comes before the buzz on this topic, simple when we talk about Data Mining. Remembering the CRISP-DM process, feature engineering (and, consequently, feature selection) is the core of a great data mining project – it comes to life on the Data Preparation phase, that is the task to have constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes. A very good definition, elegant in its simplicity, is that feature engineering is the process to create features that make machine learning algorithms work. And what makes it so important?