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40 of 46 people found the following review helpful
5.0 out of 5 stars Online Data Converts to a Plethora of Predictions
Title: Predictive Analytics - The Power to Predict Who Will Click, Buy, Lie or Die
Author: Eric Siegel
Publisher: John Wiley & Sons, Inc.
ISBN: 978-1-118-35685-2

With the astronomical mass of electronic data collected today, one may be wary of driving a GPS-tracked automobile, texting on a cellphone, purchasing grocery items with a credit card,...
Published 17 months ago by connywithay

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33 of 45 people found the following review helpful
1.0 out of 5 stars US News version of Predictive Analytics
Gives examples where people use modeling for prediction. Precious little about sample models or variables in models. For Casual reading not the serious student.
Published 16 months ago by Hassan Alam


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40 of 46 people found the following review helpful
5.0 out of 5 stars Online Data Converts to a Plethora of Predictions, February 17, 2013
This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
Title: Predictive Analytics - The Power to Predict Who Will Click, Buy, Lie or Die
Author: Eric Siegel
Publisher: John Wiley & Sons, Inc.
ISBN: 978-1-118-35685-2

With the astronomical mass of electronic data collected today, one may be wary of driving a GPS-tracked automobile, texting on a cellphone, purchasing grocery items with a credit card, posting on Facebook, anxiously blogging or clicking a mouse for information on Google. But to Eric Siegel, this collective and easily-available data is fascinating as he compiles, analyzes and predicts in his eye-opening book, "Predictive Analytics - The Power to Predict Who Will Click, Buy, Lie or Die."

In a little over three hundred pages in the hardbound book, Siegel breaks down predictive analytics (aka PA) into seven chapters with an afterword, appendices, notes, acknowledgement, author biography and index. The book is targeted from the small to large business owner, entrepreneurs, other PAers and us common folk who want to further understand how computerized data research is analyzed to predict specified outcomes and scenarios.

Cause and effect charts, illustrations along with a few comics and a glossy centerfold divulge cases of predictions in advertising, finance, healthcare, fraud, insurance, government, employment and personal venues. Some topics discussed explain ways to increase consumer buying, limit bank loan defaulting or paying off, anticipate employees quitting or clients dropping cellphone coverage along with collecting online blogs, social networking and risk information. Each chapter includes sections of "what's predicted" and "what's done about it" to show the correlation of PA and gathered data.

The author explains the art of predicting has five effects that include: a little prediction goes a long way, data is always predictive, induction is reasoning from detailed facts to general principles, ensembles compensate for limitations and persuasion can be predictable through outcomes. Using the predictive models of large corporations such as Target, Hewlett-Packard, Chase Bank, Netflix and Telenor along with John Elder's stock market techniques, Jeopardy!'s Watson computer, Kaggle's competitions, and Obama's second term presidential campaign, one learns the ins and outs of predicting through collecting and interpreting simple to complex data.

By entrusting computers to make decisions, privacy concerns are bought up, prejudices are determined and effects are manipulated when machine learning becomes the translated voice of data. Artificial intelligence can often limit overlearning, crowdsourcing and correlation pitfalls, but will it be able to always correctly interpret language, emotions and feelings of humans as it influences, persuades and molds us?

With even the book's title been subjected to analysis and written sometimes humorously of the writer's own experience of stolen identity and mockery of his geekness, it is an excellent source to any reader that sees computers overtaking and controlling our every move as we continue to be co-dependent on them as we happily benefit from increased information and understanding, attain higher profits and enjoy an easier lifestyle through such a conglomerate of PA data bytes. The only remaining question is how much PA will be gleaned from this book reviewer's post?
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27 of 31 people found the following review helpful
5.0 out of 5 stars Bringing Predictive Analytics to the masses, March 1, 2013
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This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
This book is aimed mostly at people who are interested in learning about where (as opposed to how) one can effectively use Predictive Analytics and related technologies such as Machine Learning and Natural Language Processing. There is some high level discussion of algorithms such as linear regression, decision trees, random forests and even a nice discussion about Watson's question answering algorithms. The book has many examples of where Predictive Analytics can and is being used. Some of these are relatively obscure, because companies prefer to make money off these techniques rather than talk about it (and dilute their competitive edge). The narrative is interesting and humorous, and the author shares many anecdotes from his own life, having lived through Predictive Analytics relatively short life-span. Finally, the bibliography/reference section lists URLs that will probably take you months to get through. All in all, a "popular" book aimed at people who are looking into learning about and/or adopting Predictive Analytics rather than established practitioners, but very useful and well written nevertheless.
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13 of 14 people found the following review helpful
4.0 out of 5 stars very interesting examples of applied PA, June 24, 2013
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Interesting book for beginners and people curious about PA. The examples are explained in easy and understandable way without any technical jargon. I recommend this position as a start point for those who want to apply PA. Great introduction for further more technical readings.
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8 of 8 people found the following review helpful
5.0 out of 5 stars Before reading this book I did not realize how much PA effects my every day life, June 4, 2013
This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
No matter where you are and what you do it is very likely that your behaviors are stored and used for analysis and predictions of future behaviors. A small improvement in ability to predict how people will behave can result in a big monetary gain for a company... and this means this type of behavioral analysis will continue.

These predictions are call predictive analytics (PA) and if you want to understand how it works and where it is currently used, this book gives clear explanations and explicit examples of where it is used effecting our every day life.

Before reading this book I only thought of this type of analysis in a context of displaying ads on the web or mail SPAM generators targeting people who are more likely to respond better, but this book opened my eyes to many more uses from identify fraud to identifying people who are likely to default on their mortgage.

Some uses of PA are disturbingly invasive from Target trying to predict which customers are likely to become pregnant so they sell them more baby products to Hewlett-Packard predicting which employees are more likely than others to quit their jobs. No matter how you feel about that type of predictions it is useful to know how PA does it, since this activity happens around us every day.

PA is a process by which an organization can learn from the previous experiences. It is uses historical data for modeling of what is more likely to happen in the future. Predictive analytics cannot accurately predict how any one individual will respond but it can predict how a group of people are more likely to behave in an aggregate.

How is PA different from forecasting? Forecasting makes aggregate predictions on a macro level, for example, how will economy fare or which presidential candidate will get more votes in Ohio. Whereas forecasting estimates the total number of ice cream cones to be purchased next week, predictive analytics attempts to tell which individuals will buy those cones.

I found the book not only interesting and rather eye opening - I did not realize the extend to which predictive analytics are used all around us!

Ali Julia review

Contents

Introduction
The Prediction Effect
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
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, and the Police Deduce Your Secrets
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 policy holder death? An extended sidebar on fraud detection addresses the question: how does machine intelligence flip the meaning of fraud on its head?

Chapter 3
The Data Effect: A Glut at the End of the Rainbow
We are up to our ears in data, but how much can this raw material really tell us? What actually makes it predictive? Does existing data go so far as to reveal the collective mood of the human populace? If yes, how does our emotional online chatter relate to the economy's ups and downs?

Chapter 4
The Machine That Learns: A Look Inside Chase's
Prediction of Mortgage Risk
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 machined predictions? Why couldn't prediction prevent the global financial crisis?

Chapter 5
The Ensemble Effect: Netflix, Crowdsourcing, and
Supercharging Prediction
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 crowd-sourcing 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
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
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
Ten Predictions for the First Hour of 2020
Appendices
A. Five Effects of Prediction
B. Twenty-One Applications of Predictive Analytics
C. Prediction People--Cast of "Characters"
Notes
Acknowledgments
About the Author
Index

Note: I received a copy of this book for review.
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25 of 30 people found the following review helpful
5.0 out of 5 stars Good Stuff!, February 27, 2013
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This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
I'm an IT guy. When I read and research I want solid concepts and clear explanations. This book has it all without missing a step. Even better, Siegel delivers his insight with humor and inventiveness. This book is jammed packed with real world applications for Predictive Analytics, told with a colorful, dramatic flair. Read it! Good Stuff!
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13 of 15 people found the following review helpful
5.0 out of 5 stars Predict This, February 21, 2013
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This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die is a book for anyone in any field of work. It puts into layman's terms how you can apply analytics to your business, whether that be human resources, insurance, direct sales, medical field, even teachers. I'm being predicted every day and now I know how! Thanks to Eric Siegel for writing a book on this subject that anyone can understand and its funny!
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6 of 6 people found the following review helpful
5.0 out of 5 stars The First Predictive Analytics Book for your Bookshelf, April 7, 2013
This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
Predictions have a problem. They are viewed as either magic or "snake-oil" by most people. It doesn't help that many previous predictors in society like Nostradamus and Edgar Cayce were viewed somewhat askance at best, and as charlatans at worst. Only recently has the making of predictions gained some legitimacy, and this is due to the recent rise of predictive analytics in many sectors of our society. This rather odd profession has developed out of the science of Artificial Intelligence, which seeks to capture, however crudely, some of the intelligent predictive processing capability of human brains, and express it mathematically in computer programs. Initially, predictive analytics (aka data mining) lived in the rarefied atmosphere of academics or highly-paid consultants. The challenge for predictive analytics scientists and professionals is to recast the subject in a form that is "mapped" more closely to common perceptions of what we do in our brains and why we do it. Eric Siegel has done it!

The human brain is a tricky thing to understand. I was trained initially as a Biologist, which exposed me to the view that the human brain is the most complex non-linear pattern processing system in the universe. Much of its function is devoted to prediction and decision-making, and much of it is done unconsciously. One of the biggest challenges confronting scientists of the brain is to explain its function in terms and expressions that everybody understands. It is people with brains that do things. Eric Siegel's book helps to explain some very fascinating aspects of why people do what they do, in a very engaging way. He arrays the topics in the book around five "effects" of prediction: (1) the prediction effect; (2) the data effect; (3) the induction effect; (4) the ensemble effect; and (5) the persuasion effect. To explain the nature and significance of these effects, what does he do? He tells stories. Everybody likes stores. The most popular book ever written (the Bible) is basically a story book. One of his extended stories is about John Elder, who is near and dear to my heart (many among his students and audiences think so to). Eric couches a number of predictive analytics truisms in terms of how John Elder learned them, such as stumbling over the error of "leakage from the future". John loves to wax eloquently on that mistake in his presentations of "The Top 10 Data Mining Mistakes". Another extended story is about the Watson supercomputer that won over 2 human competitors on the TV show, "Jeopardy". Eric explains how Watson did it, using the ensemble effect. An ensemble uses many mathematical techniques (algorithms) to predict an outcome, and then combines them to compose an overall prediction. Watson does not just use ensembles, Eric explains that its processing architecture consists of "an ensemble of ensembles of ensembles". That complexity would hurt my head, if Eric had not brought it down to earth in his explanation of what Watson is and how it works.

The third extended story is about... (who else) Barack Obama. Obama set up a team of data miners (as they were called then) in 2011, to be based in Chicago, and tasked with the challenge to leverage data mining technology to further his election campaign. When I saw the many ads for these data mining professionals in several online job posts, I thought, "Watch out, Republicans; he's going to eat your lunch". And, he did. Eric explains how Obama's predictive analytics team predicted those "swing voters" who had the greatest likelihood of being influenced to vote for Obama. Then, they used data from social media, like Twitter and Facebook, to predict which people were strong influencers of the swing voters; they targeted them, not the swing voters themselves (an example of the "Persuasion Effect"). That approach is at the very cutting edge of predictive analytics today, largely because of Obama's election campaign. And Eric's presentation of it makes you think, "Well, duh... of course"!

Eric Siegel has brought predictive analytics down from the intellectual stratosphere where most scientists and engineers dwell, and expressed it in terms that anyone can understand, and vended it in the form of a bunch of stories. This book should be your first predictive analytics book on your bookshelf, or to give to clients and friends when they ask, "So, what do you do"? That is the question that Eric poses in the Preface to the book, and then he marshals his stories in the rest of the book to answer it. In the University of California at Irvine Predictive Analytics Certification Program (where I teach), we require our own book ("Handbook of Statistical Analysis & Data Mining Applications", R. Nisbet, John Elder, and Gary Miner, 2012). NOW, we will require Eric Siegel's book also, and direct students to read it first! You should too.

Bob Nisbet, Ph.D.
Consulting Data Scientist
Instructor, UC-Irvine Predictive Analytics Certification Program
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11 of 13 people found the following review helpful
5.0 out of 5 stars One of the best introduction to predictive analytics, July 5, 2013
This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
In addition to being the founder of PAW, Eric is also the author of the delightful book Predictive Analytics: the power to predict who will click, buy, lie or die. As written by Thomas Davenport in the foreword, this book is "a good counterpoint to the work of Nassim Nicholas Taleb". For example, Fooled by Randomness is excellent, but pessimistic. On the opposite, Eric's book is optimistic: it shows dozens of successful predictive analytics applications.

The public of the book is rather beginner or experts with a need to get ideas about possible predictive analytics applications. As expected from the title, the topic of descriptive analytics (outlier, clustering, etc.) is not covered. However, there is way enough examples of predictive analytics to fill a book. One of the main quality of the book is to cover a (very) wide range of predictive analytics examples: marketing, health care, fraud, finance, human resources, etc.

Eric's book really makes analytics accessible. It also goes deeper with surprising analytics insights. To be noted an interesting discussion about false positive in crime prediction. In addition to covering a subject such as correlation/causation mix, it lists several examples of it. The only point I may disagree with the author is about the very definition of predictive analytics. Eric defines it using the notion of individual's behavior. To me, the word by word definition is: ANALYsis using statisTICS (ANALYTICS) for predictive purposes. Topics such as weather forecasting, predictive maintenance, etc. are examples of predictive analytics (according to the textual definition). To a larger extend, any classification/regression task in data mining and machine learning is part of predictive analytics.

To conclude, Eric's book is a journey in the world of predictive analytics. The book is delightful from the first to the last word. For example, the Watson Jeopardy story is very well explained. Beginners: jump into the field of predictive analytics with Eric's book! Experts, get a fresh view of the field and gather ideas for your own applications of predictive analytics!
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5 of 5 people found the following review helpful
5.0 out of 5 stars An Excellent Book by a Knowledgeable Author, May 29, 2013
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This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
In respect of full disclosure I have known Eric for years in his capacity as founder of the Predictive Analytics World conference, and in my work in data mining and predictive analytics. That having been said, this is an excellent book for anyone who wants to learn what predictive analytics is, and how predictive analytics may be deployed across a wide range of disciplines. If you are looking for a hardcore set of algorithms or code examples this is not the book for you, and other reviewers have commented on that. I don't think that was the point of Eric's work. Eric's work does provide a review of what I think are the main pillars of predictive analytics; data, modeling, ensembles, uplift, unstructured data, deployment and ethics. If I had an issue with this book it would be in the ordering of the chapters, but, that is my personal view, and I can see why the book was structured the way that it was. The book will help you understand the major themes of predictive analytics, written in a way that let's the reader focus on the outcome, the advantages and the possibilities around predictive analytics. It is an 'easy' read yet still contains valuable insights. If you want to understand what people are talking about when they are talking about predictive analytics, read this book.
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5 of 5 people found the following review helpful
5.0 out of 5 stars Does to Analytics what Freakonomics did to Economics, May 9, 2013
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This review is from: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hardcover)
I have several shelves of books on data mining and analytics, and have edited several books on Data Mining and Knowledge Discovery myself.

Eric's book stands out because it is not just very competently technical but also a lot of fun to read.
It is full of great quotes, real-world examples, and case studies, and will explain to the less-technical audience the power (and limitations) of Predictive Analytics and associated trend of Big Data.

The more technical audience will enjoy the chapters on The Ensemble Effect and uplift modeling ·both very hot topics with great practical results.

I highly recommend this book!
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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
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