Predictive Analytics: Modeling the World
Professor Richard DeVeaux from the Department of Mathematics and Statistics, Williams College, will deliver the first talk in the Spring 2005 UMass Amherst Operations Research / Management Science Seminar Series.
Abstract: The sheer volume and complexity of data collected or available to most organizations has created an imposing barrier to its effective use. These challenges have propelled data mining to the forefront of making profitable and effective use of data. Data mining is a process that uses a variety of data analysis and modeling techniques to discover patterns and relationships in data that may be used to make accurate predictions. While the most widespread application of data mining are in CRM (customer relationship management) some of the other important applications include fraud detection and identifying good credit risks.
But data description alone cannot provide an action plan. You must first build a predictive model based on patterns determined from known results, then test that model on results outside the original sample. In classical data analysis, the exploratory phase usually precedes the model selection phase. It’s seen as a necessary preliminary for understanding the data before beginning to think about how to model it. But in data mining, sometimes we start with a preliminary model just to narrow down the set of potential predictors. This exploratory data modeling (EDM) seems to be at odds with standard statistical practice, but, in fact, it’s simply using models as a new exploratory tool.
In this talk, we’ll take a brief tour of the current state of data mining algorithms and using several case studies to explain how EDM can be used to narrow the search for a predictive model and how data mining can add value by producing useful and meaningful results.
This series is organized by the UMASS Amherst INFORMS Student Chapter. Support for this series is provided by the Isenberg School of Management, the Department of Finance and Operations Management, and the John F. Smith Memorial Fund.