Open AccessPrediction of degree student achievement analysis
Author(s) -
Kanchan Jaiswal,
Priyanka Pathak,
Vivek Pawar,
K. D. Kharat
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/1070/1/012057
Subject(s) - graduation (instrument) , student achievement , mathematics education , logistic regression , boosting (machine learning) , computer science , academic achievement , psychology , mathematics , machine learning , geometry
Accurately estimating student upcoming achievement based on their going on educational records is critical for successfully bringing out needful pedagogical to guarantee student on time acceptable graduation. There has been ample of literature survey done on estimating in student achievement analysis, it has been faced certain problems while collecting the student data such as Student having low financial background selected course, Some courses give less detail which leads to formulating imprecise decisions and student upcoming scores should be included. In this proposed system, a machine learning function is build to estimate student achievement in graduation programs to eliminate above challenges. In this proposed system, for estimating student achievement a Logistic Regression and Extreme Gradient Boosting (XgBoost) algorithm are used. The data is collected from various junior college with computer science course as elective dataset of India to estimate student’s achievement for selecting a relevant course.