This post is co-written by Jyoti Sharma and Sharmo Sarkar from Vericast.
For any machine learning (ML) problem, the data scientist begins by working with data. This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process. One aspect of this data preparation is feature engineering.
Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it. The goal of this process is to increase the performance of the algorithm and resulting predictive model. The feature engineering process entails several stages, including feature creation, data transformation, feature extraction, and feature selection.
Building a platform for generalized feature engineering is a common task

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