Recruitment Outcome Forecasting Using Machine Learning & AI

Improving student placement rates remains a crucial challenge for higher education institutions. This paper presents a novel machine learning-based recommendation system that predicts potential placement outcomes for students within five categories: Dream Company, Core Company, Mass Recruiters, Not Eligible, and Not Interested. Leveraging historical student data, the system identifies and classifies students based on their academic performance, skills, and past placement trends. This empowers placement cells to proactively support high-potential students and guide them in honing their technical and interpersonal skills. Additionally, students themselves can utilize the system to gain insights into their likely placement outcomes and tailor their efforts accordingly. By demystifying the placement process and providing actionable insights, this system has the potential to significantly enhance student employability and optimize institutional placement strategies.