Campus Placement Prediction using Facebook Prophet and eXtreme Gradient Boost Algorithm

Colleges and universities place a premium on student job prospects since placements are often considered a measure of success. Globalization, digitalization, and new advancements in Artificial Intelligence (AI) are fascinatingly altering the job market landscape at a quicker rate than ever before. Any school would do well to familiarize itself with the current job market's demands and the most important elements influencing employability. Computer Science and Engineering (CSE) programs throughout the world are always looking for new ways to improve their curricula and better prepare their graduates for the workforce. One common method for looking into the future from the past is time-series analysis. Machine learning (ML) and fact-driven approaches are used in academic institutions. Predicting future career prospects using ML techniques is becoming an increasingly popular area of study. This research examines the potential for employment of CSE graduates taking part in campus hiring employing a time-based methodology. Based on three worldwide features- recession (g1), over-hiring (g2), and automation (g3)-this research used Facebook Prophet (FBP) and eXtreme Gradient Boost Algorithm (XGBA) to evaluate the career prospects of CSE graduates during campus hiring. The performance of FBP and XGBA was explored and assessed in terms of accuracy by considering global aspects into account. A 94.6% success rate was produced by the FBP model and XGBA after taking global variables into account. The research also found that major worldwide events, such as automation, recession, and overhiring during COVID-19, had consequences on CSE students' hiring process.