Evaluation of Classification Algorithms in Prediction of Students Performance: A Comparative Analysis

dc.contributor.advisorNagy, Dávid
dc.contributor.authorAsadova, Fidan
dc.contributor.departmentDE--Informatikai Karhu_HU
dc.date.accessioned2021-05-06T07:23:02Z
dc.date.available2021-05-06T07:23:02Z
dc.date.created2021-05-05
dc.description.abstractThe thesis covers the application and evaluation of classification algorithms to predict failures and potential academic performance of students based on Portuguese secondary school students data. The application of Educational Data Mining methods on the database of Portuguese secondary school students is promising to ascertain the factors having a dominant influence on student's academic performance and to identify students who has a high chance to fail from subject or dropout. The binary and multiclass classification approach is followed. The comparative analysis of the performance results of the four classification algorithms is covered. As performance metrics accuracy and precision are selected. The prediction results are highly influenced by first and second-period grades, when these features are discarded, all classifi cation algorithms cannot generalize the data. For future research, more features related to student socio-economical and academic background can be added, in order to achieve highly accurate classi fication solutions which will be more powerful at extracting useful patterns about student's academic performance and predicting the academic future of student more precise.hu_HU
dc.description.courseComputer Sciencehu_HU
dc.description.degreeegységes, osztatlanhu_HU
dc.format.extent40hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/308706
dc.language.isoenhu_HU
dc.subjectclassificationhu_HU
dc.subjecteducational data mininghu_HU
dc.subjectmachine learninghu_HU
dc.subject.dspaceDEENK Témalista::Informatikahu_HU
dc.titleEvaluation of Classification Algorithms in Prediction of Students Performance: A Comparative Analysishu_HU
Fájlok
Gyűjtemények