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Data Cleaning, Feature Selection,
and Data Transforms in Python
Jason Brownlee
i
Disclaimer
The information contained within this eBook is strictly for educational purposes. If you wish to apply
ideas contained in this eBook, you are taking full responsibility for your actions.
The author has made every e ort to ensure the accuracy of the information within this book was
correct at time of publication. The author does not assume and hereby disclaims any liability to any
party for any loss, damage, or disruption caused by errors or omissions, whether such errors or
omissions result from accident, negligence, or any other cause.
No part of this eBook may be reproduced or transmitted in any form or by any means, electronic or
mechanical, recording or by any information storage and retrieval system, without written permission
from the author.
Acknowledgements
Special thanks to my copy editor Sarah Martin and my technical editors Michael Sanderson and Arun
Koshy, Andrei Cheremskoy, and John Halfyard.
Copyright
Data Preparation for Machine Learning
©Copyright 2020 Jason Brownlee. All Rights Reserved.
Edition: v1.1
Contents
Copyright i
Contents ii
Preface iii
I Introduction
II Foundation
1 Data Preparation in a Machine Learning Project
1.1 Tutorial Overview
1.2 Applied Machine Learning Process
1.3 What Is Data Preparation
1.4 How to Choose Data Preparation Techniques
1.5 Further Reading
1.6 Summary
2 Why Data Preparation is So Important
2.1 Tutorial Overview
2.2 What Is Data in Machine Learning
2.3 Raw Data Must Be Prepared
2.4 Predictive Modeling Is Mostly Data Preparation
2.5 Further Reading
2.6 Summary
3 Tour of Data Preparation Techniques
3.1 Tutorial Overview
3.2 Common Data Preparation Tasks
3.3 Data Cleaning
3.4 Feature Selection
3.5 Data Transforms
3.6 Feature Engineering
3.7 Dimensionality Reduction
3.8 Further Readin
Data Preparation for Machine Learning_Jason Brownlee.pdf