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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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Recap of the levels of data

Understanding the various levels of data is necessary to perform feature engineering. When it comes time to build new features, or fix old ones, we must have ways of identifying how to work with every column.

Here is a quick table to summarize what is and isn't possible at every level:

Level of Measurement

Properties

Examples

Descriptive statistics

Graphs

Nominal

Discrete

Orderless

Binary Responses (True or False)

Names of People

Colors of paint

Frequencies/Percentages Mode

Bar

Pie

Ordinal

Ordered categories

Comparisons

Likert Scales

Grades on an exam

Frequencies

Mode

Median

Percentiles

Bar

Pie

Stem and leaf

Interval

Differences between ordered values have meaning

Deg. C or F

Some Likert Scales (must be specific)

Frequencies

Mode

Median

Mean

Standard Deviation

Bar PieStem and leaf

Box plot

Histogram

Ratio

Continuous

True 0 allows ratio statements (for example, $100 is twice as much as $50)

Money

Weight

Mean

Standard Deviation

Histogram

Box plot

The following is a table showing the types of statistics allowed...

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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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