• Anmol Pawar
• Rushikesh Patil
• Kadayya Mathapati
• Pratik Lonare
• Laxmikant Malphedwar

Making a short list of institutions to apply to might be challenging for prospective students. Students frequently question if their profile fits the requirements of a certain institution because applications are so dynamic. Additionally, because applying to college is so expensive, it is crucial that students narrow down their selection of colleges based on their profiles. Students can utilize a university admission prediction technique to figure out their odds of getting into a certain college. Data about prior applicants to other universities and their acceptance or rejection status may be used by the system. Numerous issues plagued the early iterations of these prediction algorithms, including their inability to consider important variables like research experience or GRE (Graduate Record Exam) scores. The decision to attend college is an important one, and with the admissions process becoming more competitive, there is increased interest in using machine learning to forecast admission results. This study provides a thorough review of how machine learning is being used to forecast college admissions. The research talks about the common data sources in this area, such as previous admission statistics, test scores, high school grades, extracurricular activities, recommendation letters, and personal statements Research is conducted to determine how well various machine learning techniques—such as logistic regression, neural networks, decision trees, and random forests—model and forecast admission outcomes. The research also investigates the difficulties and constraints of applying machine learning for predictions of college acceptance, including problems with data fairness, model fairness, and the intrinsically subjective character of the admissions process. According to the findings, machine learning models should be utilized in the admissions process as a supplementary tool rather than as the main factor, even if they may offer insightful forecasts and important insights. Predictive modeling fairness and ethical issues are emphasized as being essential components of this application.

College admissions, GRE scores, Selection criteria, Neural networks, Decision trees

"College Admission Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.b229-b233, November-2023, Available :http://www.jetir.org/papers/JETIR2311129.pdf

"College Admission Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppb229-b233, November-2023, Available at : http://www.jetir.org/papers/JETIR2311129.pdf