Admission IQ Using Machine Learning

Prof. Sushma ShindeDepartment of Computer Science and Engineering, Siddhant College of Engineering, Sudumbare, Pune, IndiaSiddhesh NakveDepartment of Computer Science and Engineering, Siddhant College of Engineering, Sudumbare, Pune, IndiaKrishna NimbalkarDepartment of Computer Science and Engineering, Siddhant College of Engineering, Sudumbare, Pune, IndiaShraddha KashidDepartment of Computer Science and Engineering, Siddhant College of Engineering, Sudumbare, Pune, IndiaVishwajeet JadhavDepartment of Computer Science and Engineering, Siddhant College of Engineering, Sudumbare, Pune, India

Vol 8 No 3 (2024): Volume 8, Issue 3, March 2024 | Pages: 150-154

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 01-04-2024

doi Logo doi.org/10.47001/IRJIET/2024.803020

Abstract

Making a short list of universities can be very difficult for graduate students. Undergraduates frequently ponder whether their profile fits the requirements of the universities they have their sights set on. Computer tools have been used more and more to help with college applications in recent years. Students must choose colleges that fit their profiles because college admissions are expensive. Undergraduates can use a machine learning algorithm to help them choose their ideal colleges based on a variety of factors, including university ranking, SOP, GRE, LOR, CGPA, and TOEFL scores. The algorithm can forecast a student's chances of being accepted into a certain university by accounting for each of these variables. This suggested approach can help college students make well-informed decisions about their future and improve their chances of being accepted into the University of their Choice.

Keywords

Prediction System, Stacked Ensemble Model, Machine Learning, Deep Learning, University Admission


Citation of this Article

Prof. Sushma Shinde, Siddhesh Nakve, Krishna Nimbalkar, Shraddha Kashid, Vishwajeet Jadhav, “Admission IQ Using Machine Learning” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 8, Issue 3, pp 150-154, March 2024. Article DOI https://doi.org/10.47001/IRJIET/2024.803020

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