Impact of Supervised Classification Techniques for the Prediction of Student's Performance

Abstract

Every country's concern about its growth or development is education. This field creates a way to discover hidden examples from instructive information. The authors have researched by comparing the different classification techniques on the collected dataset which is present online on the UCI Machine Learning (ML) repository. The results of this learning identify an explanatory structure uniting multiple dimensions persuading the prediction. For this research, the authors conducted the experiments on the collected dataset using the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) and measure the performance using the metrics like Accuracy (Acc.), Precision (Pr.) and Recall (Rec.). This research will also help the schools, colleges and university teachers or faculty for identifying the weak students in the class and to help them separately by conducting remedial classes or any other suitable method.