Data mining EPA's Green Vehicle Guide: Profiling and prediction using k-means clustering and neural networks

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dc.contributor.authorSmith, Tera Daun
dc.contributor.committeeChairDemuynck, Marie-Anne
dc.contributor.committeeMemberEdwards, Don E.
dc.contributor.committeeMemberMarshall, David, Ph. D.
dc.date.accessioned2017-12-14T17:33:28Z
dc.date.available2017-12-14T17:33:28Z
dc.date.issued8/30/2012
dc.description.abstractThis thesis is designed to study data mining techniques and explore the predictive value of data from the EPA's Green Vehicle Guide which supplies pertinent information regarding environmental performance for each vehicle sold in the United States from 2000 to 2010. Using IBM® SPSS® Modeler to discover patterns most advantageous to statistical analysis of the data set, each vehicle's various variables and scores in relation to emission and air quality and Smart Way status are modeled using two techniques, k-means clustering and the artificial neural networks. Predictions based on analysis of this data set are as expected with all models claiming greenhouse gas scores to be the greatest predictor variable for Smart Way status. Therefore, engineers' and companies' focus on better technology to improve greenhouse gas scores will be essential if Smart Way status and environmental consciousness is a goal.en_US
dc.identifier.urihttp://hdl.handle.net/11274/8934
dc.language.isoen_USen_US
dc.subjectPredictive data
dc.subjectPure sciencesen_US
dc.subjectGreenhouse gas scores
dc.subjectApplied sciencesen_US
dc.titleData mining EPA's Green Vehicle Guide: Profiling and prediction using k-means clustering and neural networksen_US
dc.typeThesisen_US
thesis.degree.departmentMathematics and Computer Science
thesis.degree.disciplineMathematics
thesis.degree.grantorTexas Woman's University
thesis.degree.levelMaster
thesis.degree.nameMaster of Science

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