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Predictive.Analytics.Data.Mining.and.Big.Data.pdf
Cover
Contents
Figures and Tables
Acknowledgments
1 Introduction
1.1 What are data mining and predictive analytics?
1.2 How good are models at predicting behavior?
1.3 What are the benefits of predictive models?
1.4 Applications of predictive analytics
1.5 Reaping the benefits, avoiding the pitfalls
1.6 What is Big Data?
1.7 How much value does Big Data add?
1.8 The rest of the book
2 Using Predictive Models
2.1 What are your objectives?
2.2 Decision making
2.3 The next challenge
2.4 Discussion
2.5 Override rules (business rules)
3 Analytics, Organization and Culture
3.1 Embedded analytics
3.2 Learning from failure
3.3 A lack of motivation
3.4 A slight misunderstanding
3.5 Predictive, but not precise
3.6 Great expectations
3.7 Understanding cultural resistance to predictive analytics
3.8 The impact of predictive analytics
3.9 Combining model-based predictions and human judgment
4 The Value of Data
4.1 What type of data is predictive of behavior?
4.2 Added value is what's important
4.3 Where does the data to build predictive models come from?
4.4 The right data at the right time
4.5 How much data do I need to build a predictive model?
5 Ethics and Legislation
5.1 A brief introduction to ethics
5.2 Ethics in practice
5.3 The relevance of ethics in a Big Data world
5.4 Privacy and data ownership
5.5 Data security
5.6 Anonymity
5.7 Decision making
6 Types of Predictive Models
6.1 Linear models
6.2 Decision trees (classification and regression trees)
6.3 (Artificial) neural networks
6.4 Support vector machines (SVMs)
6.5 Clustering
6.6 Expert systems (knowledge-based systems)
6.7 What type of model is best?
6.8 Ensemble (fusion or combination) systems
6.9 How much benefit can I expect to get from using an ensemble?
6.10 The prospects for better types of predictive models in the future
7 The Predictive Analytics Process
7.1 Project initiation
7.2 Project requirements
7.3 Is predictive analytics the right tool for the job?
7.4 Model building and business evaluation
7.5 Implementation
7.6 Monitoring and redevelopment
7.7 How long should a predictive analytics project take?
8 How to Build a Predictive Model
8.1 Exploring the data landscape
8.2 Sampling and shaping the development sample
8.3 Data preparation (data cleaning)
8.4 Creating derived data
8.5 Understanding the data
8.6 Preliminary variable selection (data reduction)
8.7 Pre-processing (data transformation)
8.8 Model construction (modeling)
8.9 Validation
8.10 Selling models into the business
8.11 The rise of the regulator
9 Text Mining and Social Network Analysis
9.1 Text mining
9.2 Using text analytics to create predictor variables
9.3 Within document predictors
9.4 Sentiment analysis
9.5 Across document predictors
9.6 Social network analysis
9.7 Mapping a social network
10 Hardware, Software and All that Jazz
10.1 Relational databases
10.2 Hadoop
10.3 The limitations of Hadoop
10.4 Do I need a Big Data solution to do predictive analytics?
10.5 Software for predictive analytics
Appendix A. Glossary of Terms
Appendix B. Further Sources of Information
Appendix C. Lift Charts and Gain Charts
Notes
Index
Myths, Misconceptions and Methods
Finlay Steven
ISBN: 9781137379283
DOI: 10.1057/9781137379283
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Predictive Analytics, Data Mining and Big Data
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10.1057/9781137379283 - Predictive Analytics, Data Mining and Big Data, Steven Finlay
Predictive Analytics,
Data Mining and
Big Data
Myths, Misconceptions and Methods
Steven Finlay
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10.1057/9781137379283 - Predictive Analytics, Data Mining and Big Data, Steven Finlay
© Steven Finlay 2014
All rights reserved. No reproduction, copy or transmission of this
publication may be made without written permission.
No portion of this publication may be reproduced, copied or transmitted
save with written permission or in accordance with the provisions of the
Copyright, Designs and Patents Act 1988, or under the terms of any licence
permitting limited copying issued by the Copyright Licensing Agency,
Saffron House, 6–10 Kirby Street, London EC1N 8TS.
Any person who does any unauthorized act in relation to this publication
may be liable to criminal prosecution and civil claims for damages.
The author has asserted his right to be identified as the author of this
work in accordance with the Copyright, Designs and Patents Act 1988.
First published 2014 by
PALGRAVE MACMILLAN
Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited,
registered in England, company number 785998, of Houndmills, Basingstoke,
Hampshire RG21 6XS.
Palgrave Macmillan in the US is a division of St Martin’s Press LLC,
175 Fifth Avenue, New York, NY 10010.
Palgrave Macmillan is the global academic imprint of the above companies
and has companies and representatives throughout the world.
Palgrave® and Macmillan® are registered trademarks in the United States,
the United Kingdom, Europe and other countries.
ISBN 978–1–137–37927–6
This book is printed on paper suitable for recycling and made from fully
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processes are expected to conform to the environmental regulations of the
country of origin.
A catalogue record for this book is available from the British Library.
A catalog record for this book is available from the Library of Congress.
Typeset by MPS Limited, Chennai, India.
10.1057/9781137379283 - Predictive Analytics, Data Mining and Big Data, Steven Finlay
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To Ruby and Samantha
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