Open AccessPrediction of Students Performance using Machine learning
Author(s) -
J. Dhilipan,
N. Vijayalakshmi,
S. Suriya,
A. B. Arockia Christopher
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1055/1/012122
Subject(s) - decision tree , computer science , machine learning , artificial intelligence , educational data mining , decision tree learning , entropy (arrow of time) , field (mathematics) , academic achievement , classifier (uml) , data mining , data science , mathematics education , mathematics , physics , quantum mechanics , pure mathematics
An enormous measure of computerized information is being produced over a wide assortment in the field of data mining strategies. The creation of student achievement prediction models to predict student performance in academic institutions is a key area of the development of Education Data Mining. A prediction system has been proposed by using their 10th, 12th and previous semester marks. The study is evaluated using Binomial logical regression, Decision tree, and Entropy and KNN classifier. In order to attain their higher score, this framework would assist the student to recognize their final grade and improve their academic conduct.