Understanding Feature Engineering (Part 1) — Continuous Numeric Data

Any intelligent system basically consists of an end-to-end pipeline starting from ingesting raw data, leveraging data processing techniques to wrangle, process and engineer meaningful features and attributes from this data. Then we usually leverage techniques like statistical models or machine learning models to model on these features and then deploy this model if necessary for future usage based on the problem to be solved at hand. A typical standard machine learning pipeline based on the CRISP-DM industry standard process model is depicted below.

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This article is related to

Data Visualization,2018,Data Engineering,Feature Engineering, Continuous Numeric Data