INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Analyzing and predicting academic performance is essential for any educational institution. Predicting student performance can help teachers take action to create a strategy to improve performance early. With the development of machine learning supervised methods and supervised methods that develop these types of applications help teachers better analyze students compared to existing methods. In this case student mark prediction using back-to-back project efficiency is a hypothetical guess as previous students mark and predict marks in the next lesson and calculate model accuracy. Educational institutions are using new technologies to improve the quality of education but most of the applications used in colleges are related to services and development rather than web applications that help students to do online training and exams. There are many ways teachers can learn more about student performance. In view of this problem machine learning methods are used to predict students' marks based on past marks and to predict the outcome. Lower descent models are used to predict student performance and predict marks in the next lesson
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"Student Performance Analysis using Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 4, page no.165 - 168, April-2022, Available :http://www.ijsdr.org/papers/IJSDR2204029.pdf
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Publication Details:
Published Paper ID: IJSDR2204029
Registration ID:200213
Published In: Volume 7 Issue 4, April-2022
DOI (Digital Object Identifier):
Page No: 165 - 168
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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