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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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The four levels of data

We already know that we can identify data as being either qualitative or quantitative. But, from there, we can go further. The four levels of data are:

The nominal level
The ordinal level
The interval level
The ratio level

Each level comes with a varying level of control and mathematical possibilities. It is crucial to know which level data lives on because it will dictate the types of visualizations and operations you are allowed to perform.

The nominal level

The first level of data, the nominal level, has the weakest structure. It consists of data that are purely described by name. Basic examples include blood type (A, O, AB), species of animal, or names of people. These types of data are all qualitative.

Some other examples include:

In the SF Job Salary dataset, the Grade column would be nominal
Given visitor logs of a company, the first and last names of the visitors would be nominal
Species of animals in a lab experiment would be nominal

Mathematical operations allowed

At...

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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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