International Journal of Progressive Research in Engineering Management and Science
(Peer-Reviewed, Open Access, Fully Referred International Journal)

ISSN:2583-1062
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Paper Details

Student Performance Analysis using Machine Learning (KEY IJP************043)

  • Saurav Kumar Gupta,Ishu Kumar,Manish Kumar,Sonam Kumari,Suchitra Devi A

Abstract

Universities are facing significant challenges in analyzing their studentsperformance due to the vast amount of digital dataavailable from sources like social media, research, agriculture, and medical records. Admission, student placement, andcurriculum are among the most crucial challenges, with data analysis primarily taking place during admission and placementprocesses. A university's market position and reputation depend heavily on its studentsacademic performance, placement, andother factors. To better comprehend student performance and categorize them, this project is using processing techniques. Whilemost universities have their management systems to manage student records, lecturers at UNIMAS lack access due to privacysettings. The proposed Student Performance Analysis System (SPAS) aims to resolve this issue by tracking studentsresultsand using a predictive system to identify those likely to perform poorly in their courses. The system employs data miningtechniques, primarily classification, to establish principles for predicting student performance.

DOI LINK : 10.58257/IJPREMS31181 https://www.doi.org/10.58257/IJPREMS31181
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