Assessing Students for Industry Readiness using Classification Methods - Hossain Molla, Jakir; Basak, Sandip Kumar; Obaidullah, Sk Md; Alam, Parveen Ahmed; Goto, Takaaki; Sen, Soumya (ACIT '22: Proceedings of the 9th International Conference on Applied Computing & Information Technology, 2022)
Although Placement of students is an intrinsic requirement for the students worldwide majorly for professional as well as for higher degree courses. However, often the students are not aware about their skill levels that are considered as the industry readiness parameters. The Institutions often take care of the students to upgrade the skill level to meet the requirement of the Industry. In order to analyze the students based on the several parameters of industry readiness intelligent methods are required to assess them. In this research work, different classification methods are applied on the existing placement data to evaluate whether a student will get a job or not. Accuracies of these methods are compared using a real life data set. This analysis will help the Institutes to take the decision how long the training programs will continue. The result of the classification methods have been improved further using Random oversampling.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Classification; Decision Tree; Placement data; Random Forest; Random oversampling