Recruitment System with Placement Prediction Using Machine Learning

Project Description & Abstract

Recruitment System with Placement Prediction is an intelligent web-based application designed to streamline the college recruitment and placement management process for students, recruiters, and Training & Placement Officers (TPOs). The system provides a centralized platform for managing student profiles, recruitment requirements, candidate selection, and placement-related activities. Traditional placement processes often rely on manual data management and resume-based candidate screening, which can be time-consuming and may make it difficult for recruiters to evaluate the overall performance of candidates. This system addresses these challenges by analyzing candidate information and using Machine Learning to support data-driven recruitment decisions. The proposed system uses the Random Forest Regressor algorithm to analyze various candidate performance attributes and generate a placement prediction score. Instead of considering only a student's resume, the system can evaluate factors such as academic performance, technical skills, aptitude, communication skills, projects, internships, certifications, and other relevant candidate attributes. Recruiters can define their job requirements and obtain a list of suitable candidates based on their skills and predicted placement performance. The system can help recruiters filter, rank, and shortlist candidates for further recruitment rounds, reducing the amount of manual effort required during campus placement drives. Students can also benefit from the system by viewing their placement prediction and understanding the areas in which they can improve. This can help them identify their strengths and weaknesses and prepare more effectively for upcoming recruitment opportunities. The system includes a web-based interface developed using Flask, allowing students, recruiters, and administrators to interact with the platform. Student and recruitment information can be stored in a database and accessed according to the user's role. The machine learning component can be trained using historical placement data. The Random Forest model combines predictions from multiple decision trees to provide a more stable prediction compared with relying on a single decision tree. The resulting prediction can be used as a supporting metric for candidate evaluation and recruitment decisions. Overall, the proposed system aims to make the recruitment process faster, more organized, data-driven, and efficient while providing students and recruiters with useful insights into placement performance. Tags : Recruitment System, Placement Prediction, Placement Management System, Campus Recruitment, Student Placement, Machine Learning, Random Forest, Random Forest Regressor, Candidate Selection, Candidate Screening, Recruitment Management, Training and Placement, TPO Portal, Student Career Management, Placement Probability, Candidate Ranking, Skill Matching, Career Prediction, Artificial Intelligence, Python, Flask, MySQL, Scikit-learn