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Proven Practices in Predictive Modeling

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Proven Practices for Predictive Modeling
presented by Mary-Elizabeth Eddlestone, SAS Institute, Inc.

This presentation discusses overall best practices for predictive modeling and advanced analytics.

In our ongoing quest for “analytics excellence,” what are some of the strategies and tactics that we, as analytics practitioners, can consider, not only for individual predictive modeling projects, but for increasing the value and importance of analytics in our organizations? This presentation will share some of the common strategies, attributes, processes and best practices of the most successful organizations. Best practices will include considerations for an overall analytics process as well as the discrete steps of building a predictive model, such as: data preparation and sampling; input (variable) examination, selection, and transformation; model selection and validation; and more.

Presented at the SAS Users of Oregon Conference 2013 in Salem, OR.
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