Advancing Employability Predictions in Autonomous Institutions Through Machine  Learning: A Contrast with Non-Autonomous Institutes with reference to technical  institutes

Author(s)	Nishigandha Bhalekar
Country	India
Abstract	Education system is undergoing significant transformation, evolving societal needs, and a shift towards more student-centric learning. The employment of students is major concern of any educational institutes. In today’s Era many technical institutes enhanced Academic Standards by availing autonomy. Autonomy offers the freedom to innovate, improve academic quality, and respond to the evolving needs of students and the industry. As compared to non-autonomous technical institutes. Institutes become more flexible, student-centric, and research-oriented and hence promoting accountability and continuous improvement of a students. As a result, autonomy can lead to better academic outcomes, better employability for students, and a stronger institutional reputation. The use of machine learning is helpful to forecast which student will and will not be employed specially in autonomy. This research will compare use of ML to predict employability ration in technical autonomous and technical non-autonomous institutes by considering few parameters.
Keywords	Employability, Employability Prediction, autonomy, non-autonomous institutes, Machine learning, Machine Learning
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-03
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.38207
Short DOI	https://doi.org/g86wn6

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