About the Book
Mesmerizing & fascinating...The Seattle Post-Intelligencer "The Freakonomics of big data." Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating surprisingly accessible introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a how to for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data.
Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction now in its Revised and Updated edition former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: * What type of mortgage risk Chase Bank predicted before the recession. * Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. * Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights.
* Five reasons why organizations predict death including one health insurance company. * How U.S. Bank and Obama for America calculated and Hillary for America 2016 plans to calculate the way to most strongly persuade each individual. * Why the NSA wants all your data: machine learning supercomputers to fight terrorism. * How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! * How companies ascertain untold, private truths how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. * How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. *183 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.
A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it or consumed by it get a handle on the power of Predictive Analytics.
Table of Contents:
Foreword Thomas H. Davenport xiii Preface xv What is the occupational hazard of predictive analytics? Introduction: The Prediction Effect 1 Chapter 1: Liftoff! Prediction Takes Action (deployment) 17 Chapter 2: With Power Comes Responsibility: Hewlett-Packard, Target, and the Police Deduce Your Secrets (ethics) 37 Chapter 3: The Data Effect: A Glut at the End of the Rainbow (data) 67 Chapter 4: The Machine That Learns: A Look Inside Chase s Prediction of Mortgage Risk (modeling) 103 Chapter 5: The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles) 133 Chapter 6: Watson and the Jeopardy! Challenge (question answering) 151 Chapter 7: Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift) 187 Afterword 218 Ten Predictions for the First Hour of 2020 Appendices A. Five Effects of Prediction 221 B. Twenty-One Applications of Predictive Analytics 222 C. Prediction People Cast of "Characters" 225 Notes 228 Acknowledgments 290 About the Author 292 Index 293
