Applying SMOTE with Decision Tree Classifier for Campus Placement Prediction

Abstract

It is the dream of every student to attain an excellent career with decent remuneration. It will be an additional benefit if they get a high-profile job during their campus placement before they leave. The campus placement activities with the right resources at the right time and with minimal cost are of the greatest benefit to undergraduates regardless of any stream viz. engineering, business, medical, or sciences. The scope of the paper is to prepare an automated model that predicts or analyzes the probability of students getting positioned in a company by salient parameters like academic performance in terms of CGPA, test marks, or other professional degree evaluations and another non-academic parameter such as gender. For this intention, one of the classification algorithms named Decision Tree and up sampling technique “Synthetic Minority Oversampling Technique” had been used. The outcome of this analysis shall lend a hand to the organization to propose an approach that enhances the performance of students to get a better job in the pre-final years.