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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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Toc

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

Identifying missing values in data

Our first method of identifying missing values is to give us a better understanding of how to work with real-world data. Often, data can have missing values due to a variety of reasons, for example with survey data, some observations may not have been recorded. It is important for us to analyze our data, and get a sense of what the missing values are so we can decide how we want to handle missing values for our machine learning. To start, let's dive into a dataset that we will be interested in for the duration of this chapter, the Pima Indian Diabetes Prediction dataset.

The Pima Indian Diabetes Prediction dataset

This dataset is available on the UCI Machine Learning Repository at:

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