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Feature Engineering Made Easy

You're reading from   Feature Engineering Made Easy Identify unique features from your dataset in order to build powerful machine learning systems

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781787287600
Length 316 pages
Edition 1st Edition
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Authors (2):
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Divya Susarla Divya Susarla
Author Profile Icon Divya Susarla
Divya Susarla
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (10) Chapters Close

Preface 1. Introduction to Feature Engineering FREE CHAPTER 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 9. Other Books You May Enjoy

Feature Understanding – What's in My Dataset?

Finally! We can start to jump into some real data, some real code, and some real results. Specifically, we will be diving deeper into the following ideas:

  • Structured versus unstructured data
  • Quantitative versus qualitative data
  • The four levels of data
  • Exploratory data analysis and data visualizations
  • Descriptive statistics

Each of these topics will give us a better sense of the data given to us, what is present within the dataset, what is not present within the dataset, and some basic notions on how to proceed from there.

If you're familiar with, Principles of Data Science, much of this echoes Chapter 2, Types of Data of that book. That being said, in this chapter, we will specifically look at our data less from a holistic standpoint, and more from a machine-learning standpoint.

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