Automation for Identification of Academic Factor for Non Placements using Efficient Machine Learning Algorithms

The reputation and institutional standing of a college are greatly impacted by the success of job placements.Student achievement levels show wide differences across universities.Some learners excel remarkably, others achieve average results, and numerous individuals encounter various challenges during their studies.Several factors influence job market success after graduation.These include grades, verbal skills, technical knowledge, and individual characteristics.Most existing studies focus on building predictive algorithms using programming tools like Python and R to forecast employment outcomes.However, these approaches typically generate probability scores without explaining why certain graduates remain unemployed.Existing methods fail to reveal the root causes behind placement failures.This research develops analytical frameworks that identify specific educational weaknesses contributing to unsuccessful job searches.Rather than simply calculating odds, our approach uncovers particular academic gaps that prevent students from securing positions in their field.Our system does more than just predict employment success; it also predicts placement outcomes and pinpoints specific areas of weakness for students who do not get hired.We apply efficient supervised learning algorithms such as Random forest to process educational datasets collected from wwwkagglecom, www.dataworld.com,www.github.com,www.datagov.inetc, achieving reliable results with student datasets.Students with internet access and college placement offices benefit from this web-based application.Educational institutions can develop focused assistance programs by utilizing the platform to identify particular learning weaknesses in students who haven't secured placements.Colleges can then establish specific help systems to fix these problems and improve the number of students getting jobs.This is done by enhancing how students are prepared for their careers.