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Predictive Analytics – The Power to Predict Who Will Click, Buy, Lie or Die Reviewed By Conny Crisalli of Bookpleasures.com
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Conny Withay







Reviewer Conny Withay:Operating her own business in office management since 1991, Conny is an avid reader, volunteers reading the Bible to the elderly, and makes handmade jewelry. A cum laude graduate with a degree in art living in the Pacific Northwest, she is married with two sons, two daughter-in-laws, and one granddaughter.

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By Conny Withay
Published on February 18, 2013
 


Author: Eric Siegel
Publisher: John Wiley & Sons, Inc.
ISBN: 978-1-118-35685-2






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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