Predictive analytics the power to predict who will click buy lie or die 1st edition by Eric Siegel ISBN 1119145686 9781119145684

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Product details:

ISBN 10:      1119145686

ISBN 13: 978-1119145684 

Author:     Eric Siegel

“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 (aka machine learning) 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(aka machine learning) 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 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.
• 182 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.

 

Predictive analytics the power to predict who will click buy lie or die 1st Table of contents:

Chapter 1: Liftoff! Prediction Takes Action

• Going Live
• A Faulty Oracle Everyone Loves
• Predictive Protection
• A Silent Revolution Worth a Million
• The Perils of Personalization
• Deployment’s Detours and Delays
• In Flight
• Elementary, My Dear: The Power of Observation
• To Act Is to Decide
• A Perilous Launch
• Houston, We Have a Problem
• The Little Model That Could
• Houston, We Have Liftoff
• A Passionate Scientist
• Launching Prediction into Inner Space

Chapter 2: With Power Comes Responsibility

• The Prediction of Target and the Target of Prediction
• A Pregnant Pause
• My 15 Minutes
• Thrust into the Limelight
• You Can’t Imprison Something That Can Teleport
• Law and Order: Policies and Policing of Data
• The Battle over Data
• Data Mining Does Not Drill Down
• HP Learns about Itself
• Insight or Intrusion?
• Flight Risk: I Quit!
• Insights: The Factors behind Quitting
• Delivering Dynamite
• The Value Gained from Flight Risk
• Predicting Crime to Stop It Before It Happens
• The Data of Crime and the Crime of Data
• Machine Risk without Measure
• The Cyclicity of Prejudice
• Good Prediction, Bad Prediction
• The Source of Power

Chapter 3: The Data Effect

• A Cautionary Tale: Orange Lemons
• The Source: Otherwise Boring Logs Fuel Prediction
• Social Media and Mass Public Mood
• Recycling the Data Dump
• The Instrumentation of Everything We Do
• Batten Down the Hatches: TMI
• Who’s Your Data?
• The Data Effect: It’s Predictive
• The Building Blocks: Predictors
• Far Out, Bizarre, and Surprising Insights
• Caveat #1: Correlation Does Not Imply Causation
• Caveat #2: Securing Sound Discoveries
• What Went Wrong: Accumulating Risk
• The Potential and Danger of Automating Science: Vast Search
• A Failsafe for Sound Results
• A Prevalent Mistake
• Putting All the Predictors Together

Chapter 4: The Machine That Learns

• Boy Meets Bank
• Bank Faces Risk
• Prediction Battles Risk
• Risky Business
• The Learning Machine
• Building the Learning Machine
• Learning from Bad Experiences
• How Machine Learning Works
• Decision Trees Grow on You
• Computer, Program Thyself
• Learn Baby Learn
• Bigger Is Better
• Overlearning: Assuming Too Much
• The Conundrum of Induction
• The Art and Science of Machine Learning
• Feeling Validated: Test Data
• Carving out a Work of Art
• Putting Decision Trees to Work for Chase
• Money Grows on Trees
• The Recession-Why Microscopes Can’t Detect Asteroid Collisions
• After Math

Chapter 5: The Ensemble Effect

• Casual Rocket Scientists
• Dark Horses
• Mindsourced: Wealth in Diversity
• Crowdsourcing Gone Wild
• Your Adversary Is Your Amigo
• United Nations
• Meta-Learning
• A Big Fish at the Big Finish
• Collective Intelligence
• The Wisdom of Crowds . . . of Models
• A Bag of Models
• Ensemble Models in Action
• The Generalization Paradox: More Is Less
• The Sky’s the Limit

Chapter 6: Watson and the Jeopardy! Challenge

• Text Analytics
• Our Mother Tongue’s Trials and Tribulations
• Once You Understand the Question, Answer It
• The Ultimate Knowledge Source
• Artificial Impossibility
• Learning to Answer Questions
• Walk Like a Man, Talk Like a Man
• Putting on the Pressure
• The Answering Machine
• Moneyballing Jeopardy!
• Amassing Evidence for an Answer
• Elementary, My Dear Watson
• Mounting Evidence
• Weighing Evidence with Ensemble Models
• An Ensemble of Ensembles
• Machine Learning Achieves the Potential of Natural Language Processing
• Confidence without Overconfidence
• The Need for Speed
• Double Jeopardy! – Would Watson Win?
• Jeopardy! Jitters: Deploying a Prototype
• For the Win
• After Match: Honor, Accolades, and Awe
• Iambic IBM AI
• Predict the Right Thing

Chapter 7: Persuasion by the Numbers

• Churn Baby Churn
• Sleeping Dogs
• A New Thing to Predict
• Eye Can’t See It
• Perceiving Persuasion
• Persuasive Choices
• Business Stimulus and Business Response
• The Quantum Human
• Predicting Influence with Uplift Modeling
• Banking on Influence
• Predicting the Wrong Thing
• Response Uplift Modeling
• The Mechanics of Uplift Modeling
• How Uplift Modeling Works
• The Persuasion Effect
• Influence across Industries
• Immobilizing Mobile Customers

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