Methodical Implementation of Data Mining Classifiers and ANN for Prediction of Accomplishment of Student Education

doi:10.23940/ijpe.23.09.p4.587597

Abstract

Abstract: In recent years, artificial intelligence applications in education have expanded daily. The study and implementation knowledge of this is necessary for building advanced applications based on AI that help in the education sector. The first objective of this research paper is to identify the various factors that help predict student performance related to background, education, and psychology. The second objective is to study and analyze the various data mining classifiers (Decision Tree, Naive Bayes, Logistic Regression, SVM, Random Forest, and KNN) and artificial neural networks on the dataset, including 689 records of students for the prediction of student performance in Python programming language. The conclusion of the evaluation metric of all algorithms suggests that ANN implementation is best for classifying student performance as either high or low. It predicts 96% accuracy.

Key words: artificial neural network, COVID, education, intelligence, performance, student

Mini Agarwal and Bharat Bhushan Agarwal. Methodical Implementation of Data Mining Classifiers and ANN for Prediction of Accomplishment of Student Education [J]. Int J Performability Eng, 2023, 19(9): 587-597.

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