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LEADER 00000cam a2200685Ii 4500
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008 160419s2016 njua ob 001 0 eng d
019 988812087
020 9781119153658
020 1119153654
020 1119145678
020 9781119145677
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029 1 DEBSZ|b485797844
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035 (OCoLC)946943495|z(OCoLC)988812087
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049 INap
082 04 303.49
082 04 303.49|qOCoLC|223/eng/20230216
099 eBook O'Reilly for Public Libraries
100 1 Siegel, Eric,|d1968-|eauthor.|1https://id.oclc.org/
worldcat/entity/E39PCjxKrJtXGdXYrQYFTw7crm
245 10 Predictive analytics :|bthe power to predict who will
click, buy, lie, or die /|cEric Siegel.|h[O'Reilly
electronic resource]
250 Revised and updated.
264 1 Hoboken, New Jersey :|bJohn Wiley & Sons,|c[2016]
264 4 |c©2016
300 1 online resource (1 volume) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
500 Revised edition of the author's Predictive analytics,
2013.
504 Includes bibliographical references and index.
505 8 Machine generated contents note: Foreword Thomas H.
Davenport xiii Preface to the Revised and Updated Edition
What's new and who's this book for -- the Predictive
Analytics FAQ Preface to the Original Edition xv What is
the occupational hazard of predictive analytics?
Introduction The Prediction Effect 1 How does predicting
human behavior combat risk, fortify healthcare, toughen
crime fighting, and boost sales? Why must a computer learn
in order to predict? How can lousy predictions be
extremely valuable? What makes data exceptionally
exciting? How is data science like porn? Why shouldn't
computers be called computers? Why do organizations
predict when you will die? Chapter 1 Liftoff! Prediction
Takes Action (deployment) 17 How much guts does it take to
deploy a predictive model into field operation, and what
do you stand to gain? What happens when a man invests his
entire life savings into his own predictive stock market
trading system? Chapter 2 With Power Comes Responsibility:
Hewlett-Packard, Target, the Cops, and the NSA Deduce Your
Secrets (ethics) 37 How do we safely harness a predictive
machine that can foresee job resignation, pregnancy, and
crime? Are civil liberties at risk? Why does one leading
health insurance company predict policyholder death? Two
extended sidebars reveal: 1) Does the government undertake
fraud detection more for its citizens or for self-
preservation, and 2) for what compelling purpose does the
NSA need your data even if you have no connection to crime
whatsoever, and can the agency use machine learning
supercomputers to fight terrorism without endangering
human rights? Chapter 3 The Data E ffect: A Glut at the
End of the Rainbow (data) 67 We are up to our ears in
data. How much can this raw material really tell us? What
actually makes it predictive? What are the most bizarre
discoveries from data? When we find an interesting insight,
why are we often better off not asking why? In what way is
bigger data more dangerous? How do we avoid being fooled
by random noise and ensure scientific discoveries are
trustworthy? Chapter 4 The Machine That Learns: A Look
Inside Chase's Prediction of Mortgage Risk (modeling) 103
What form of risk has the perfect disguise? How does
prediction transform risk to opportunity? What should all
businesses learn from insurance companies? Why does
machine learning require art in addition to science? What
kind of predictive model can be understood by everyone?
How can we confidently trust a machine's predictions? Why
couldn't prediction prevent the global financial crisis?
Chapter 5 The Ensemble Effect: Netflix, Crowdsourcing, and
Supercharging Prediction (ensembles) 133 To crowdsource
predictive analytics -- outsource it to the public at
large -- a company launches its strategy, data, and
research discoveries into the public spotlight. How can
this possibly help the company compete? What key
innovation in predictive analytics has crowdsourcing
helped develop? Must supercharging predictive precision
involve overwhelming complexity, or is there an elegant
solution? Is there wisdom in nonhuman crowds? Chapter 6
Watson and the Jeopardy! Challenge (question answering)
151 How does Watson -- IBM's Jeopardy!-playing computer --
work? Why does it need predictive modeling in order to
answer questions, and what secret sauce empowers its high
performance? How does the iPhone's Siri compare? Why is
human language such a challenge for computers? Is
artificial intelligence possible? Chapter 7 Persuasion by
the Numbers: How Telenor, U.S. Bank, and the Obama
Campaign Engineered Influence (uplift) 187 What is the
scientific key to persuasion? Why does some marketing
fiercely backfire? Why is human behavior the wrong thing
to predict? What should all businesses learn about
persuasion from presidential campaigns? What voter
predictions helped Obama win in 2012 more than the
detection of swing voters? How could doctors kill fewer
patients inadvertently? How is a person like a quantum
particle? Riddle: What often happens to you that cannot be
perceived, and that you can't even be sure has happened
afterward -- but that can be predicted in advance?
Afterword 218 Eleven Predictions for the First Hour of
2022 Appendices A. The Five Effects of Prediction 221 B.
Twenty Applications of Predictive Analytics 222 C.
Prediction People -- Cast of "Characters" 225 Notes 228
Acknowledgments 290 About the Author 292 Index 293 .
520 "Predictive analytics unleashes the power of data. With
this technology, computers literally learn from data how
to predict future behaviors of individuals. In this
updated and revised edition of Predictive Analytics,
former Columbia University professor and Predictive
Analytics World founder Eric Siegel reveals the power and
perils of prediction. New material includes: - The Real
Reason the NSA Wants Your Data: Automatic Suspect
Discovery. A special sidebar in Chapter 2, "With Power
Comes Responsibility," presumes--with much evidence--that
the National Security Agency considers PA a strategic
priority. Can the organization use PA without endangering
civil liberties? - Dozens of new examples from Facebook,
Hopper, Shell, Uber, UPS, the U.S. government, and more.
The Central Tables' compendium of mini-case studies has
grown to 182 entries, including breaking examples. - A
much needed warning regarding bad science. Chapter 3, "The
Data Effect," includes an in-depth section about an all-
too-common pitfall, and how we avoid it, i.e., how to
successfully tap data's potential without being fooled by
random noise, ensuring sound discoveries are made. - Even
more extensive Notes, updated and expanded to 70+ pages,
now moved to an online PDF. Now located at
www.predictivenotes.com, the Notes include citations and
comments that cover the above new content, as well as new
citations for many other topics"--|cProvided by publisher.
588 0 Print version record.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Social sciences|xForecasting.
650 0 Economic forecasting.
650 0 Prediction (Psychology)
650 0 Social prediction.
650 0 Human behavior.
650 6 Sciences sociales|xPrévision.
650 6 Prévision économique.
650 6 Prédiction (Psychologie)
650 6 Prévision sociale.
650 6 Comportement humain.
650 7 human behavior.|2aat
650 7 Economic forecasting|2fast
650 7 Human behavior|2fast
650 7 Prediction (Psychology)|2fast
650 7 Social prediction|2fast
776 08 |iPrint version:|aSiegel, Eric, 1968-|tPredictive
analytics.|bRevised and updated.|dHoboken, New Jersey :
Wiley, [2016]|z9781119145677|w(DLC) 2015031895
|w(OCoLC)923728054
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781119145677/?ar
|zAvailable on O'Reilly for Public Libraries
938 Askews and Holts Library Services|bASKH|nAH28825134
994 92|bJFN