Prediction for University Admission using Machine Learning

Abstract

In today's education world there are many number of students who want to pursue higher education after engineering or any graduate degree course. Higher education in the sense, some people want to do M.tech through GATE or through any educational institute entrance examination and some people want to do MBA through CAT or through any respective educational institute entrance examination and some people want to do Masters in abroad universities. we are focusing on only the students who want to pursue their higher education in abroad universities. Generally Higher education in abroad universities means we have many options like canada, USA ,UK Germany, Italy, Australia etc. But we are focusing on only the students who want to do their Masters in America. Students who want to do masters in America have to write GRE (Graduate Records Examination) and TOEFL/IELTS (Test of English as a Foreign Language/International English Language Testing System). Once they have attended the exams they have to prepare their SOP(statement of purpose) and LOR(letter of reccomendation) which are one of the crucial factors they have to consider. These LOR and SOP plays a vital role if the student was looking for any scholarship. Then the students have to choose the universities they want to study or apply, we cannot apply to all the universities that will lead to lot of application fees. Here comes the problem that the student dontt know to which university he might get admission. There are some online blogs which help in these matter but they are not that much accurate and dont consider all the factors and there are some consultancy offices which will take lot of our money and time and sometimes they will give some false information.so our goal is to develop a model which will tell the students their chance of admission into a respective university. This model should consider all the crucial factors which plays a vital role in student admission process and should have high accuracy. The model name is UAP. To access this model we will develop a simple user interface.