Use of Linear Discriminant Analysis (LDA), K Nearest Neighbours (KNN), Decision Tree (CART), Random Forest (RF), Gaussian Naive Bayes (NB), Support Vector Machines (SVM) to Predict Admission for Post Graduation Courses

Abstract

Today’s world has become really competitive when it comes to higher education.There are many options that bewilder students in the selection of their university. There are many consultancy facilities that help students in choosing the optimal university according to their needs.Many algorithms can be used to build a recommendation system . In this paper we will be attempting to compare various machine learning algorithms that cater to the needs of building a recommendation system that will give the probability of admission of a student applying for Masters abroad. Various algorithms like Linear Discriminant Analysis (LDA), K Nearest Neighbours (KNN), Decision Tree (CART), Random Forest (RF), Gaussian Naive Bayes (NB) ,Support Vector Machines (SVM) will be compared in this paper.The results of the system will help the student in making the correct choice and apply for the most suitable university.