7 of the Most Used Feature Engineering Techniques

Feature engineering describes the process of formulating relevant features that describe the underlying data science problem as accurately as possible and make it possible for algorithms to understand and learn patterns. Each feature describes a kind of information "piece". The sum of these pieces allows the algorithm to draw conclusions about the target variables -- at least if you have a data set that actually contains information about your target variable. According to the Forbes magazine, Data Scientists spend about 80% of their time collecting and preparing relevant data, with the data cleaning and data organizing alone taking up about 60% of the time. But this time is well spent.