Machine learning based university admit eligibility predictor
Abstract
There are a lot of students in the modern educational system who need to pursue further education after taking an undergraduate certification course. Advanced education in the sense that some groups having an undergraduate degree in Engineering must complete their Masters degree through either GATE or CAT or any other entrance examination conducted by the individual institutes either in national level or in the international level to get the admission. In educational institutions, the question of understudy confidentiality is crucial. In order to foresee the probability that a undergraduate would be conceded to a Master's program, we are working with AI models. This will enable students to plan ahead and determine if they will have the chance to be recognised. There are three significant Machine learning models particularly Linear regression, Decision tree regression and Random Forest regression. In this paper we will predict the admissions using Random Forest algorithm, a well-known supervising learning model.
- Publication:
-
American Institute of Physics Conference Series
- Pub Date:
- January 2024
- DOI:
- 10.1063/5.0181764
- Bibcode:
- 2024AIPC.2802l0047R