PlaceMe: An Integrated Campus Recruitment Platform

Abstract

An Integrated Campus Recruitment Platform aims to streamline and modernize the recruitment process for educational institutions by integrating machine learning-driven recommendations with dynamic industry skill updates. Traditional placement prediction systems rely on static datasets that quickly become outdated due to the ever-evolving nature of the IT and job market. To address this, PlaceMe introduces an admin-controlled interface where industry-specific skill requirements can be regularly updated via file uploads. This allows for real-time retraining of the machine learning model, ensuring that student skill-gap analysis and recommendations remain aligned with current market demands. The platform automates the entire campus recruitment workflow—from job application submission to sending congratulatory emails upon selection—thereby reducing manual effort and ensuring transparency. By combining dynamic skill management with a smooth recruitment experience, PlaceMe empowers students with actionable insights and institutions with an efficient, scalable recruitment solution.

Introduction

Context & Problem:
Traditional campus recruitment systems are often fragmented, inefficient, and rely on outdated static datasets, making it difficult to keep up with rapidly changing industry skill requirements—especially in dynamic fields like IT.

Proposed Solution – PlaceMe:
PlaceMe is an integrated, intelligent campus recruitment platform that automates the full recruitment lifecycle—from student registration and job application to shortlisting, interview scheduling, and final placement. It features a machine learning-powered skill comparison engine that administrators can update dynamically with new job-specific skills, retraining the model in real time. This keeps recommendations current and relevant to evolving industry demands.

Key Features:

• Dynamic skill updates via an admin interface.

• Resume parsing and skill extraction using NLP.

• Predictive placement scoring using Random Forest algorithms.

• Automated notifications to students and recruiters.

• Analytics dashboard for monitoring placement trends.

• Streamlined communication between students, companies, and placement officers.

Terminology & Techniques:
PlaceMe uses NLP for resume parsing and skill extraction, Random Forest for prediction, and predictive analytics for placement chances. Admins manage job postings, skills, and retrain models through a secure panel.

Literature Survey:
Related studies highlight the benefits of machine learning in predicting student placement, automating recruitment processes, enhancing communication, and improving skill recommendations.

Methodology:
The platform collects and preprocesses student data, extracts skills, performs feature engineering, and trains a Random Forest model for job matching and recommendations. Notifications and workflow automation support smooth communication. The system is cloud-deployed for scalability.

Implementation:
PlaceMe has three primary user roles: Admin, Recruiter, and Student, each with specific functionalities—such as managing skills, job postings, and applications respectively. The system calculates placement scores and suggests skill improvements if needed.

Results & Discussion:
PlaceMe effectively predicts placement chances, adapts to updated skills, reduces administrative workload, and enhances communication. While some challenges remain (like parsing accuracy), it offers a flexible, dynamic, and efficient recruitment solution aligned with modern industry needs.

Conclusion

PlaceMe is a smart campus recruitment platform that uses machine learning and natural language processing (NLP) to make the placement process more effective and student-friendly. The system reads student resumes and extracts important details like skills, academic background, and project experience using NLP. Then, it uses a Random Forest model to analyze this data and predict how likely a student is to get placed based on the current job requirements. If a student’s chances are low, the system doesn’t stop there — it goes one step further and suggests what skills the student should learn or improve to become a better match for the job roles available. This way, students receive personalized feedback that helps them grow. Admins can also update the required skills for each job role and retrain the model with just a file upload, keeping the system up-to-date with the latest industry trends. Overall, PlaceMe makes recruitment smoother, smarter, and more helpful for both students and institutions.

References

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Copyright

Copyright © 2025 Dr. Sarika Jadhav, Sachin Barvekar, Aditya Gaikwad, Omkar Chavan, Mahesh Gaikwad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.