Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16751
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dc.contributor.authorKadu, Varun-
dc.contributor.authorMishra, Pawan Kumar-
dc.contributor.authorDandhare, Sarthak-
dc.contributor.authorPatni, Jagdish Chandra-
dc.contributor.authorChandel, Palash-
dc.contributor.authorPathak, Soham-
dc.contributor.authorBahadure, Nilesh-
dc.date.accessioned2024-12-12T09:29:56Z-
dc.date.available2024-12-12T09:29:56Z-
dc.date.issued2024-
dc.identifier.isbn9798350373783-
dc.identifier.urihttps://doi.org/10.1109/OTCON60325.2024.10687450-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16751-
dc.description.abstractPredicting and enhancing student performance has been a crucial topic of concentration in education amid the quick development of technology and the increasing need for higher-quality instruction. The use of machine learning technology to forecast pupils' academic success is covered in detail in this study report. This research project examined several prediction models that reliably predict students' performance according to a range of extracurricular and academic attributes. An overview of machine learning methods is given in this paper to automate the process of predicting students' academic performance and extracting outcomes. © 2024 IEEE.en_US
dc.language.isoenen_US
dc.publisher2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectDecision Treeen_US
dc.subjectDecision Tree Methoden_US
dc.subjectEntropyen_US
dc.subjectK-Nearest Neighbouren_US
dc.subjectKnnen_US
dc.subjectRegression Modelen_US
dc.subjectSupport Vector Machineen_US
dc.subjectTree Classifieren_US
dc.titleStudent'S Performance Prediction Using Machine Learning Algorithms- a Comparative Studyen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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