By: Dean Abbott, Abbott Analytics and SmarterHQ, Inc.
Portions excerpted from Chapter 2 of his book Applied Predictive Analytics (Wiley 2014, http://amzn.com/1118727967) Successful predictive modeling is more than identifying the right algorithms. And, even though 60-90% of our time is spend on data preparation before deploying the first predictive model built from a new data set, successful predictive modeling goes well beyond effective
Our prior article on this venue began outlining the business value for solving “the other churn” – employee attrition. We introduced the “quantitative scissors” with a simple model of employee costs, benefit, and breakeven points. The goal...
Big Data is not a singular concept but rather a label for a range of data issues. A few months ago I wrote an article about the Volume, Velocity, and Variety (and other “V’s”) of big...
Last month I made the case for discussing model deployment. One of the mistakes I see organizations make related to deployment is this: after the model is deployed, there is little or no thought about that model...
The Cross Industry Standard Process for Data Mining (CRISP-DM) is the leading published methodology for Data Mining (DM), and by extension, Predictive Analytics (PA). I use it routinely as I lead PA projects and when I...
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