Candidate Prediction and Ranking System for Recruitment Management

AI-Powered Candidate Prediction and Ranking System is an advanced AI-driven model that has been created to enhance the effectiveness and impartiality of the contemporary recruitment process. It combines machine learning (ML), natural language processing (NLP), and predictive analytics to evaluate and rank applicants to a job in a uniform and clear manner. The conventional approach to hiring is in many cases slow, subjective and do not facilitate consistency and this may result in poor hiring. In order to eliminate these concerns, the given model assesses candidates based on organized information, which was collected in various areas: academic background (CGPA, attendance, arrears), technical factors (Python, JavaScript, C, C++, MySQL), aptitude results (logical, numerical, and verbal reasoning), and placement experience (on campus/off campus activity, internship experience, and selection history). Multi-criteria decision analysis (MCDA) is used in the analytical process together with more sophisticated algorithms like the Random Forest, XGBoost, and Neural Networks. A group method then refines the forecasts and reduces bias and enhances ranking. On the whole, the system will result in more secure, data-driven recruitment process that can lead to better quality of decisions made and makes organizations more able to target the right candidates.