Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
The Quant’s Dilemma: Subjectivity In Predictive AI’s Value
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To Deploy Predictive AI, You Must Navigate These Tradeoffs
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Data Analytics in Higher Education
 Universities confront many of the same marketing challenges as...
How Generative AI Helps Predictive AI
 Originally published in Forbes, August 21, 2024 This is the...
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By: Dean Abbott, President, Abbott Analytics 

 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 teach appiled DM and PA courses. It was the subject of three KD-Nuggets polls, in 2002, 2004, and 2007) and nearly half of the responders stated they used it as the main methodology for DM (http://www.kdnuggets.com/polls/2007/data_mining_methodology.htm). There are six stages in the CRISP-DM process, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. There are many excellent books describing five

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