Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Student performance prediction helps teachers understand how students may perform in exams. Finding weak students early can improve their results. This project predicts student performance without using machine learning. It uses simple data like attendance, test marks, and assignment scores. Basic rules and calculations are used to predict whether a student’s performance is good or poor. The system is easy to use and useful for schools and colleges.
Keywords:
Educational Data Mining (EDM), Student Performance Prediction, without using Machine Learning, Random Forest, Feature Selection, Academic Success, Early Intervention, Learning Analytics.
Cite Article:
"Student Performance Predictor", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 2, page no.a229-a231, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602027.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator