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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
A Hybrid Machine Learning Approach for Predicting Academic Performance of Rural Students
Authors
Ritesh Kandari
Keywords
Academic Performance, Machine Learning, Rural Education, Random Forest, Support Vector Machine, Prediction Model, Student Risk Analysis
Abstract
This research proposes a hybrid machine learning model to predict academic performance of students in rural colleges. The model integrates Random Forest and Support Vector Machine algorithms to analyze various academic and socio-economic factors. The dataset includes parameters such as attendance, internal assessment scores, family income, internet access, and parental education level. The aim is to develop a predictive system that can assist educators in identifying students at academic risk and provide timely interventions. Results show over 89% prediction accuracy using the hybrid model.
IJCRT's Publication Details
Unique Identification Number - IJCRT2506622
Paper ID - 289372
Page Number(s) - f395-f396
Pubished in - Volume 13 | Issue 6 | June 2025
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Ritesh Kandari, "A Hybrid Machine Learning Approach for Predicting Academic Performance of Rural Students", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 6, pp.f395-f396, June 2025, Available at :http://www.ijcrt.org/papers/IJCRT2506622.pdf
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Volume 13 | Issue 12
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December 2025
Volume 13 | Issue 12
Last Date :
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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