• Sravani Gorusu
• Harsha Vardhini Eluri
• Joshitha Dwarapudi
• Charanya Dadala
• Sowanya Kumari Nalam

Prospective graduate students always face a dilemma deciding universities of their choice while applying to master's programs. While there are a good number of predictors and consultancies that guide a student, they aren't always reliable since decision is made on the basis of select past admissions. In this project, we present a Machine Learning based method where we use algorithm, SVM (Support Vector Machine), given the profile of the student to predict colleges based on their profile. Results then indicate if the university of choice is can be accepted or rejected. Using this method user can enter various factors as input like GRE Score, GRE Score Quant, GRE Score verbal, under-graduation score, Work experience, Ranking, TOFEL score, total technical papers published. Based on these features machine learning model can be selected and predictions of which college is possible for applying for post-graduation is calculated and displayed to user.

Machine Learning, Support Vector Machine, GRE Score, TOFEL score, Work Experience, GRE score Quant, GRE score verbal.

"Machine Learning Model for Prediction of Post Graduate Admissions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.f51-f57, July-2021, Available :http://www.jetir.org/papers/JETIR2107637.pdf

"Machine Learning Model for Prediction of Post Graduate Admissions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppf51-f57, July-2021, Available at : http://www.jetir.org/papers/JETIR2107637.pdf