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Siegel E. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

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Siegel E. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Wiley, 2013. — 338 p. — ISBN: 1118356853, 9781118356852
This book is easily understood by all readers. Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.
You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.
Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.
How? Prediction is powered by the world's most potent, booming 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 — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.
What is the occupational hazard of predictive analytics?
Introduction: The Prediction Effect
Liftoff! Prediction Takes Action (deployment)
With Power Comes Responsibility: Hewlett-Packard,Target, and the Police Deduce Your Secrets (ethics)
The Data Effect: A Glut at the End of the Rainbow(data)
The Machine That Learns: A Look Inside Chase’sPrediction of Mortgage Risk (modeling)
The Ensemble Effect: Netflix, Crowdsourcing, andSupercharging Prediction (ensembles)
Watson and the Jeopardy! Challenge (questionanswering)
Persuasion by the Numbers: How Telenor, U.S. Bank,and the Obama Campaign Engineered Influence (uplift)
Ten Predictions for the First Hour of 2020
About the Author
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