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  Published Paper Details:

  Paper Title

Unlocking Success: A Smart System for Predicting Student Performance and Recommending Courses

  Authors

  Aayushi Patel,  Sanika Pathak,  Nidhi Khadke,  Nayanshree Purbia,  Shreya Mukherjee

  Keywords

student, performance, prediction, recommendation, machine learning, linear regression

  Abstract


This research paper describes a system for predicting student performance and recommending courses using predictive analytics. The system utilizes machine learning models such as Linear Regression, Support Vector Regression (SVR), and Random Forest to accurately forecast student performance based on historical academic data, study habits, and course preferences. Moreover, it generates personalized course recommendations specific to each student's profile, academic goals, and learning needs. Experimental evaluation demonstrates the effectiveness of the proposed approach in predicting performance and providing relevant course suggestions, which can significantly enhance academic outcomes and student satisfaction.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT24A4567

  Paper ID - 258361

  Page Number(s) - n549-n555

  Pubished in - Volume 12 | Issue 4 | April 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Aayushi Patel,  Sanika Pathak,  Nidhi Khadke,  Nayanshree Purbia,  Shreya Mukherjee,   "Unlocking Success: A Smart System for Predicting Student Performance and Recommending Courses", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 4, pp.n549-n555, April 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A4567.pdf

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Call For Paper July 2024
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
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