Placement Readiness Check: Predicting Placement Status Using Supervised ML Methods

Abstract

Every student who joins a professional course has two primary goals in mind, one is securing good score in examinations; but the second one has more importance in their life which is getting well-paid job in reputed organization through campus placement. Campus Placements play a vital role in every engineering student's life. Every year about a million engineering graduates from different institution in India. Each one of them dreams of a perfect job in their desired company. But, in reality, only a few students about 3% from million get high-scale job in reputed organization. In the field of information technology, the demand for qualified college grads is increasing by the day. The majority of undergrads, on the other hand, are unprepared to satisfy the expectations of the IT business. The number of engineering students who meet a company's requirements and quality standards is quite low. In today's society, competition for acquiring medium to high scale placement is increasing day by day. Preparing in this competitive society, students become anxious, and start having a feeling of uncertainty regarding their skills, placement preparation and as a result most of them lose confidence. They have a strong desire to know where they stand based on their current placement preparation. Thus, in this paper, a campus placement predictor system is proposed which will predict the campus placement chance as well as estimate the salary package range one can get from campus placements based on his/her academic scores as well as most recent assessment scores. Using this, students can evaluate themselves about their placement chance and possible package with respect to current preparation. The ability to predict the placement status that undergraduates are more likely to obtain will encourage them to work harder in order to make optimal progress toward a career in various technology sectors.