Fueling Machine Learning with Feature Engineering – Jigyasa Grover, Twitter


In the contemporary world of learning algorithms – “data is the new oil”. Data demands efficient refinement to expose valuable information. To lay a strong foundation for the state-of-the-art machine learning algorithms to work their magic, the crude oil-like data needs to be infused with domain knowledge and extracted into “features”. This talk aims to introduce the audience to the subject of Feature Engineering, and talk about the power of the most creative aspect of data science which often does not get its due limelight. It will also walk the audience through the process of feature engineering as done in formal settings with a simple hands-on Pythonic example on publicly available data, along with putting forward some popular techniques like hashing, encoding, and embedding, which assists in pulling the most out of the data after giving it a proper structure for predictive modeling. Terms pertaining to the realm of feature engineering like relevance, selection, combination, and explosion will also be discussed. The goal is to institute the importance of data, especially in its worthy format, and the spell it casts on fabricating smart learning algorithms.

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