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Feature Engineering Made Easy

You're reading from Feature Engineering Made Easy

Product type Book

Published in Jan 2018

Publisher Packt

ISBN-13 9781787287600

Pages 316 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (2):

Sinan Ozdemir

Divya Susarla

View More author details

Table of Contents (14) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Introduction to Feature Engineering

2. Feature Understanding – What's in My Dataset?

3. Feature Improvement - Cleaning Datasets

4. Feature Construction

5. Feature Selection

6. Feature Transformations

7. Feature Learning

8. Case Studies

1. Other Books You May Enjoy

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Quantitative versus qualitative data

To accomplish our diagnoses of the various types of data, we will begin with the highest order of separation. When dealing with structured, tabular data (which we usually will be doing), the first question we generally ask ourselves is whether the values are of a numeric or categorical nature.

Quantitative data are data that are numerical in nature. They should be measuring the quantity of something.

Qualitative data are data that are categorical in nature. They should be describing the quality of something.

Basic examples:

Weather measured as temperature in Fahrenheit or Celsius would be quantitative
Weather measured as cloudy or sunny would be qualitative
The name of a person visiting the White House would be qualitative
The amount of blood you donate at a blood drive is quantitative

The first two examples show that we can describe similar systems using data from both the qualitative and quantitative side. In fact, in most datasets, we will be working with...

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Authors (2)

Sinan Ozdemir

Sinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.

See other products by Sinan Ozdemir

Divya Susarla

Divya Susarla is an experienced leader in data methods, implementing and applying tactics across a range of industries and fields including investment management, social enterprise consulting, and wine marketing. She trained in data by way of specializing in Economics and Political Science at University of California, Irvine, cultivating a passion for teaching by developing an analytically based, international affairs curriculum for students through the Global Connect program. Divya is currently focused on natural language processing and generation techniques at Kylie.ai, a startup helping clients automate their customer support conversations. When she is not busy working on building Kylie.ai and writing educational content, she spends her time traveling across the globe and experimenting with new recipes at her home in Berkeley, CA.

See other products by Divya Susarla

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