Analysis of Predictive Models for Post Graduate Admission

Authors

  • Sarrah Master Students, St Vincent Pallotti College of Engineering & Technology College, Nagpur
  • Anshul Ghate Students, St Vincent Pallotti College of Engineering & Technology College, Nagpur
  • Aabhas Wasnik Students, St Vincent Pallotti College of Engineering & Technology College, Nagpur
  • Nikhil Patle Students, St Vincent Pallotti College of Engineering & Technology College, Nagpur
  • Prof. Pallavi Wankhede 2 Faculty, St Vincent Pallotti College of Engineering & Technology College, Nagpur

Keywords:

Random Forest Regression, Support Vector Regression, Linear Regression, Chance of Admission

Abstract

Higher education has been increasingly popular among students globally in recent years. However, applying for post-graduation courses may be difficult, and many students are uncertain about which colleges to seek, which foreign tests to take, and what cut off grades are necessary. Many new graduates are confused with the admission standards and procedures, and they may have to pay a large amount of money to consultancies to assist them in determining their chances of admission. The restricted number of colleges that a human consultant may assess, on the other hand, can result in biased and inaccurate recommendations. To address these concerns, this article compares three regression model learning methods, including random forest regression, support vector regression and linear regression that estimates a student's chance of admission to their selected colleges based on their profile. The main goal is to help graduates discover and target colleges most suited to their profiles. The study aims to find the best accurate and error-free model for predicting post-graduation admission. 

References

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Published

29-04-2023

How to Cite

Sarrah Master, Anshul Ghate, Aabhas Wasnik, Nikhil Patle, & Prof. Pallavi Wankhede. (2023). Analysis of Predictive Models for Post Graduate Admission. International Journal for Research Publication and Seminar, 14(3), 78–83. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/471

Issue

Vol. 14 No. 3 (2023): Vol 14 | Issue 3 | NCASIT 2023

Section

Original Research Article

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