PREDICTING GRADUATE ADMISSIONS USING MACHINE LEARNING TECHNIQUES

Abstract

Many students in today's educational environment seek to continue their education after completing an engineering or graduate degree programme. Some people are interested in higher education in the sense that they wish to complete their M. Tech through the GATE entrance exam or another admission exam for a school. Some students desire to pursue an MBA through the Common Admission Test (CAT) or through the entrance exam for their chosen educational institution, while others seek to pursue a master's degree at an international university. Higher education often implies we have numerous possibilities, including Canada, the United States, the United Kingdom, Germany, Italy, Australia, etc. Graduate Records Examination (GRE) and TOEFL/IELTS (Test of English as a Foreign Language/International English Language Testing System) scores are required of students who choose to pursue master's degrees overseas. One of the most important things students must think about is preparing their SOP (Statement of Purpose) and LOR (Letter of Recommendation) once they have taken the tests. If the student was applying for a scholarship, the LOR and SOP are crucial. The pupils must next decide which institutions they wish to attend or apply to; we cannot apply to all universities because doing so would incur significant application expenses. The student's lack of knowledge about the institution he could be admitted to is now a concern. There are certain internet blogs that may be helpful in these situations, but they are not always correct and don't take all the relevant elements into account. There are also some consulting firms that will demand a lot of our time and money and occasionally provide inaccurate information. Our objective is to create a machine learning model that will estimate a student's likelihood of admission to a certain university based on their test results and other relevant data.