An Analytical Approach to Predict Employability Status of Students

Abstract

Abstract One of the major concerns of the students after graduation is the job opportunities offered to them. Not only students, but also the universities are inclined towards maximizing the job offers for their students through campus recruitment drives. Against this background, the scope of this study is to gauge the performance of top four known classification techniques of data mining, which are, Decision tree, Random forest, Naive Bayes & KNN. These machine learning algorithms are applied on students’ data, collected from the university database of Manipal University Jaipur and student models are created which will predict the employability status of students in future and discover factors which will significantly contribute to their employability. After applying and studying the ac- curacies of these algorithms, we have found that Random forest behaves better than the rest of the algorithms with 89% accuracy.