AI-Powered Campus Placement Portal for Efficient Recruitment
1st Mrs M Vasuki 1*,2nd Dr. T. Amalraj Victoire*,3nd A Anbhazhaghi2
1Associate Professor, Department of Computer Applications, Sri Manakula Vinayagar Engineering College (Autonomous), Puducherry 605008, India
vasukimca@smvec.ac.in
2Associate Professor, Department of Computer Applications, Sri Manakula Vinayagar Engineering College (Autonomous), Puducherry 605008, India
amalrajvictoire@gmail.com
3Post Graduate student, Department of Computer Applications, Sri Manakula Vinayagar Engineering College (Autonomous), Puducherry 605008, India
anbhazhaghiarul@gmail.com
Abstract: The process of campus placement which is vital for going from education to work, gets stuck often due to ineffective candidate shortlisting, excessive waiting for communication and manual efforts in admin tasks. As a result of these problems, hiring can take more time, candidates may end up in roles that aren’t a good fit for them and there might not be enough transparency in the process. This project focuses on solving these challenges by developing a Campus Placement Portal with XGBoost-based ML techniques to result in more accurate and efficient selection of candidates. The portal gathers students, employers and academic institutions in one place which helps to simplify the whole process of campus recruitment. Using predictive analytics and decisions driven by data, the system helps to shortlist the best candidates by matching their skills, work history and how well they fit the job. With this method, employers and students can monitor how the matching process is going as it happens and this speeds up the process as well. The use of machine learning in campus recruiting greatly improves efficiency and pushes forward a new standard for future campus placement processes. To keep improving and growing, the system works to place students in good jobs and help employers easily hire the right candidates.
Keywords: Campus Placement Portal, XG Boost, Recruitment Process, Candidate Shortlisting, Predictive Analytics, Student Profiling, AI in Recruitment
