QNT 735 - Predictive Modeling

3 credit(s)
Predictive modeling is the process of developing mathematical tools or models that generate an accurate prediction. The models covered in this course include: data pre-processing, over-fitting and model tuning, measuring performance in regression models, non-linear regression models like neural networks, adaptive regression splines, support vector machines, K-nearest neighbors, regression trees and rule-based models. These modeling techniques will be applied to large data sets from different business areas to support business decision making. This course will offer the basic concepts and techniques through a series of case studies. Either R or SAS software will be used in applications to arrive at the appropriate decision.
Prerequisite(s): QNT 601.
Laboratory fee.

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