Introduction to Predictive Modeling

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This course will introduce you to the basic concepts in predictive modeling, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. In both cases, predictive modeling takes data where a variable of interest is known and develops a model that relates this variable to a series of predictor variables. In classification, the variable of interest is categorical ("purchased something" vs. "has not purchased anything"). In prediction, the variable of interest is continuous ("dollars spent"). Five techniques will be used: k-nearest neighbors, classification and regression trees (CART), neural nets, logistic regression and multiple linear regression. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model). The course includes hands-on work with XLMiner, a data-mining add-in for Excel.

Please note this course lasts four weeks until 3rd May 2013.

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