Student Performance Prediction by means of Multiple Regression
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Abstract
Education organizations incorporate and store enormous volumes of information, for example, student involvement, enrolment records, and academic records. Mining such information yields fortifying data that serves to institutional administrators. Hefty data yields ambiguity for Educational Data Mining (EDM) which reduces the correctness of prediction model applied over EDM. Subsequently going through numerous literature we came into conclusion that sorting out data into category is a characteristic decision. Essentially, arranging data into category is prevalent in numerous logical fields as per institutional administrator’s requirement. In this research we have projected a prediction model as Multiple Regression and by means of curve fitting we found the relation of independent and dependent variable with lesser MSE which will predict student performance parameters.
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