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FUNDAMENTALS OF MACHINE LEARNING FOR PREDICTIVE DATA ANALYTICS
FUNDAMENTALS OF MACHINE LEARNING FOR PREDICTIVE DATA ANALYTICS
Algorithms, Worked Examples, and Case Studies
John D. KelleherBrian Mac NameeAoife D’Arcy
The MIT PressCambridge, MassachusettsLondon, England
© 2015 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form or by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
MIT Press books may be purchased at special quantity discounts for business or sales promotional use. For information, please email special_sales@mitpress.mit.edu
This book was set in the programming language by the author. Printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Kelleher, John D., 1974
Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / John D. Kelleher, Brian Mac Namee, and Aoife DArcy.
   pages cm
Includes bibliographical references and index.
ISBN 978-0-262-02944-5 (hardcover : alk. paper) 1. Machine learning. 2. Data mining. 3. Prediction theory. I. Mac Namee, Brian, 1978 II. DArcy, Aoife, 1978 III. Title.
Q325.5.K455 2015
006.3’1—dc23
2014046123
10 9 8 7 6 5 4 3 2 1
To my wife and family,thank you for your love, support, and patience.John
To my family.
Brian
To Grandad D’Arcy, for the inspiration.Aoife
Contents
Copyright
Preface
Structure of the Book
How to Use this Book
Online Resources
Acknowledgements
Notation
Notational Conventions
Notational Conventions for Probabilities
1      Machine Learning for Predictive Data Analytics
1.1   What Is Predictive Data Analytics?
1.2   What Is Machine Learning?
1.3   How Does Machine Learning Work?
1.4   What Can Go Wrong with Machine Learning?
1.5   The Predictive Data Analytics Project Lifecycle: CRISP-DM
1.6   Predictive Data Analytics Tools
1.7   The Road Ahead
1.8   Exercises
2      Data to Insights to Decisions
2.1   Converting Business Problems into Analytics Solutions
2.1.1   Case Study: Motor Insurance Fraud
2.2   Assessing Feasibility
2.2.1   Case Study: Motor Insurance Fraud
2.3   Designing the Analytics Base Table
2.3.1   Case Study: Motor Insurance Fraud
2.4   Designing and Implementing Features
2.4.1   Different Types of Data
2.4.2   Different Types of Features
2.4.3   Handling Time
2.4.4   Legal Issues
2.4.5   Implementing Features
2. ............

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