Student Performance Measure by using Different Classification Methods of Data Mining

dc.contributor.authorChoudhary, Neha
dc.contributor.supervisorMishra, Ashutosh
dc.date.accessioned2016-08-10T09:51:20Z
dc.date.available2016-08-10T09:51:20Z
dc.date.issued2016-08-10
dc.descriptionMaster of Engineering-Software Engineeringen_US
dc.description.abstractThe assessment in outcome based learning is very vital and significant approach toward measuring the student’s performance. There are many traditional methods existing in this context. The data mining is one of the intelligent computing methods which are having widely accepted features that enable the idea of its usage in assessment. Much work has been done to measure the student performance by using different methodologies and modern technologies. In this work, we have gone through the current data sets of students of the university and different classification methods of data mining are used to measure the accuracy of student performance. Based on the analysis of the result, it has been concluded that accuracy and the other measures of SVM is more than the other classification methods.en_US
dc.identifier.urihttp://hdl.handle.net/10266/4058
dc.language.isoenen_US
dc.subjectStudent performanceen_US
dc.subjectClassificationen_US
dc.subjectEducational Data Miningen_US
dc.subjectComputer Scienceen_US
dc.subjectSoftware engineeringen_US
dc.titleStudent Performance Measure by using Different Classification Methods of Data Miningen_US
dc.typeThesisen_US

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