: Using "uplift modeling" to identify which voters can be most effectively persuaded.
Written by former Columbia University professor and founder of Machine Learning Week, Eric Siegel, this bestselling book provides a non-technical introduction to how machine learning and big data are used to predict human behavior.
: Persuasion can be predicted through outcomes (uplift modeling). Key Real-World Examples Predictive Analytics: The Power to Predict Who ...
: How the retailer deduces a customer is pregnant from shopping patterns.
: How the company predicts which employees are about to quit. Chase Bank : Predicting mortgage risk and prepayment. : Using "uplift modeling" to identify which voters
The book explains that predictive analytics is not about "perfect" forecasting but about "putting odds on the future" to drive more effective decisions. Siegel introduces : The Prediction Effect : A little prediction goes a long way. The Data Effect : Data is always predictive.
Predictive Analytics Basics: 6 Introductory Terms & 5 Effects Key Real-World Examples : How the retailer deduces
: How predictive modeling allowed a computer to beat human champions on Jeopardy! . Shopping Options