Open AccessAn Analytical Approach to Predict Employability Status of Students
Author(s) -
Barjinder Singh Saini,
Ginika Mahajan,
Harish Sharma,
Ziniya
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1099/1/012007
Subject(s) - employability , graduation (instrument) , naive bayes classifier , decision tree , random forest , scope (computer science) , computer science , machine learning , mathematics education , artificial intelligence , medical education , psychology , data science , mathematics , pedagogy , support vector machine , medicine , geometry , programming language
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.