Ozdemir Sinan - Feature engineering made easy: identify unique features from your dataset in order to build powerful machine learning systems

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First published: January 2018

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Sinan Ozdemir is a data scientist, start-up founder, and educator living in the San Francisco Bay Area. He studied pure mathematics at Johns Hopkins University. He then spent several years conducting lectures on data science at Johns Hopkins University before founding his own start-up, Kylie.ai, which uses artificial intelligence to clone brand personalities and automate customer service communications.

Sinan is also the author of Principles of Data Science, available through Packt.

Divya Susarla is an experienced leader in data methods, implementing and applying tactics across a range of industries and fields, such as investment management, social enterprise consulting, and wine marketing. She studied business economics and political science at the University of California, Irvine, USA.

Divya is currently focused on natural language processing and generation techniques at Kylie.ai, a start-up helping clients automate their customer support conversations.

Michael Smith uses big data and machine learning to learn about how people behave. His experience includes IBM Watson and consulting for the US government. Michael actively publishes at and attends several prominent conferences as he engineers systems using text data and AI. He enjoys discussing technology and learning new ways to tackle problems.

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This book will cover the topic of feature engineering. A huge part of the data science and machine learning pipeline, feature engineering includes the ability to identify, clean, construct, and discover new characteristics of data for the purpose of interpretation and predictive analysis.

In this book, we will be covering the entire process of feature engineering, from inspection to visualization, transformation, and beyond. We will be using both basic and advanced mathematical measures to transform our data into a form that's much more digestible by machines and machine learning pipelines.

By discovering and transforming, we, as data scientists, will be able to gain a whole new perspective on our data, enhancing not only our algorithms but also our insights.

This book is for people who are looking to understand and utilize the practices of feature engineering for machine learning and data exploration.

The reader should be fairly well acquainted with machine learning and coding in Python to feel comfortable diving into new topics with a step-by-step explanation of the basics.

, Introduction to Feature Engineering , is an introduction to the basic terminology of feature engineering and a quick look at the types of problems we will be solving throughout this book.

, Feature Understanding What's in My Dataset?, looks at the types of data we will encounter in the wild and how to deal with each one separately or together.