A comparative study of automated undergraduate engineering admission prediction in an Indian university using machine learning

Meenakshi Gupta (), Alpana (), Prinima Gupta () and Neeraj Varshney ()
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Meenakshi Gupta: Manav Rachna University
Alpana: Amity University
Prinima Gupta: Manav Rachna University
Neeraj Varshney: GLA University

Journal of Computational Social Science, 2025, vol. 8, issue 3, No 4, 22 pages

Abstract: Abstract Most of the students who want to consider a career in engineering or technology aim to get admission to top engineering colleges and universities. Engineering entrance exams are considered one of the toughest exams not only in India but all over the world. For the same, students enroll themselves in various coaching institutes, which promise admission. It has been noted that even with the most rigorous tutoring and extensive study sessions, the majority of students are unable to pass engineering entrance tests, which prevents them from being admitted to the college of their choice. The purpose and novelty of this study are to show how different factors, including secondary and senior secondary percentages, preparation methods, family background, school board type, drop cases, and aid, affect engineering admissions and to offer strategies for increasing the increasing the overall chances of being admitted to the best Indian colleges.The dataset used in this work is the Indian University in the northern region specializes in engineering which has more than 1400 records samples with different attributes that are used to decide whether a student will get admission or not. The outcome of the proposed methodology is evaluated using the classification methods for engineering admission prediction and classification. It has been shown that the proposed model (random forest classifier) attained an accuracy of 87% and worked appropriately for the chosen admission data set. Hence, the random forest model may support the mentioned factors affecting the screening of engineering admission to support students as well as parents for better and early-stage college prediction and corrective measures for universities too.

Keywords: Undergraduate admission; Exploratory data analysis; Engineering; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s42001-025-00384-w

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