Investigation and application of analytical methods for prediction using classification models. Topics will include neural networks, logistic regression, data-driven misclassification costs, and segmentation models. Further topics may include k-nearest neighbor classification, advanced decision tree algorithms, QUEST, CHAID, naive Bayes classification and Bayesian networks, cost-benefit analysis for trinary and k-nary models. market basket analysis, and association rules.
Prerequisites: DATA 511 or permission of department chair.
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