Anticipating Future College Admission Cutoffs: An Innovative Predictive Model Incorporating Student Reviews and Historical Admissions Cutoff Data Using Machine Learning

In this era of ever-increasing competition for the higher education opportunities, the capability to predict future college admission cutoff ranks plays an important role in shaping the educational journeys of prospective students. This work presents an innovative predictive model aimed at assisting students in making well-informed decisions when it comes to college selection. The proposed framework harnesses the power of key elements: student-personal reviews and historical admission cutoff, National Institutional Ranking Framework (NIRF) factors. This predictive model combines the student reviews, with the insights of admission cutoff ranks and NIRF scores. This work showcases a comprehensive approach that not only anticipates future admission cutoffs but also empowers students to make enlightened enrolment choices. This study explores the intricate interplay between student feedback and historical trends, providing a comprehensive perspective on the evolving landscape of college admissions.