A Web-Based Placement Portal with Web Data Mining for Recruiter Insights

Senthil Pandian P.*, Hemalatha J.**
*-** Department of Computer Science and Engineering, AAA College of Engineering and Technology, Amathur, Sivakasi, Tamil Nadu, India.
Periodicity:January - June'2025

Abstract

This paper proposes Talent Track, a centralized web-based placement portal designed to streamline and digitize campus recruitment for colleges and universities. The platform allows students to create profiles, upload academic records, resumes, and certificates, which are securely stored and accessible only to verified recruiters. By integrating web data mining techniques, the portal enhances the recruitment process by enabling recruiters to search, filter, and match student profiles based on specific skills, qualifications, and academic performance. This data-driven approach saves time and helps companies identify the most suitable candidates efficiently. College staff and administrators can monitor placement activities through a dashboard, generate reports, and track student performance, reducing paperwork and improving coordination. Overall, the system automates key operations, ensures secure data handling, and bridges the gap between education and employment through smart, data-centric recruitment.

Keywords

Campus Recruitment, Candidate Management, Skill Matching, RBAC, Secure Login.

How to Cite this Article?

Pandian, P. S., and Hemalatha, J. (2025). A Web-Based Placement Portal with Web Data Mining for Recruiter Insights. International Journal of Data Mining Techniques and Applications, 14(1), 26-33.

References

[3]. Hann, J., & Kamber, M. (2000). Data Mining: Concepts and Techniques. Morgan Kaufmann.
[4]. Kumar, A. V. (Ed.). (2016). Web Usage Mining Techniques and Applications across Industries. IGI Global.
[7]. Mane, M. A., Al-Hammadi, M. A. M., & Pawar, V. D. (2021). Recommender system for online job portal. International Journal for Research & Development in Technology, 15 (2), 48-53.
[9]. Muthukumar, K. K. M., & Pandian, S. (2022). Analyzing and improving the performance of decision database with en-hanced momentous data types. Asia Journal of Information Technology, 16(9), 699-705.
[13]. Ramesh, V., Parkavi, P., & Yasodha, P. (2011). Performance analysis of data mining techniques for placement chance prediction. International Journal of Scientific & Engineering Research, 2(8), 1-7.
[14]. Singh, J., Goyal, S. B., Kaushal, R. K., Kumar, N., & Sehra, S. S. (2024). Applied Data Science and Smart Systems. Taylor & Francis Group.
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