Candidate Sorting and Personalized Rejection System Using Random Forest Algorithm

Abstract

With the accelerated recruitment environment in today's business world, employers are challenged by the dual test of processing applications quickly in high numbers while maintaining fairness and transparency. Conventional hiring practices are subjective and do not provide much feedback to rejected candidates, hence dissatisfaction and wasted talent. The following paper proposes an intelligent recruitment system named Candidate Sorting and Personalized Rejection Feedback System. At its core, the system employs a Random Forest algorithm to rank and score candidates against different parameters such as educational qualifications, competencies, experience in the workplace, and anticipated salaries. The AI-driven feedback module, which is one of the highlight features of the platform, is developed as a rule-based engine that creates automatically customized improvement suggestions for rejected candidates. Unlike classical models ending in rejection, our site enables professional growth through giving applicant’s constructive feedback on real profile weaknesses. The integration of machine learning, rule-based reasoning, and HR analytics not only improves the hiring process but redefines candidate experience—making recruitment a two-way value-exchange process for recruiters and applicants.