Placement Data Analysis System: A Web-Based Analytics Platform for Campus Recruitment Management

Colleges across India routinely manage campus placement records using isolated spreadsheet files, resulting in fragmented data, zero analytical capability, and no differentiated access for students versus administrators. This paper presents the Placement Data Analysis System (PDAS), a full-stack web application built on Python Flask, Pandas, NumPy, MySQL, and Chart.js. PDAS delivers a centralized, role-differentiated platform offering dual-pathway data ingestion (manual web-form entry and bulk CSV upload), automated statistical metric computation, and real-time browser-rendered dashboards. A two-tier Role-Based Access Control (RBAC) mechanism grants administrators full CRUD capabilities while allowing students to access aggregated analytics without a password. The system computes and displays five key metrics—placement percentage, average CTC, highest CTC, top recruiting companies, and year-on-year trends—on each dashboard load. Experimental validation against a 51-record dataset confirmed correctness of all modules, with bulk ingestion completing in under two seconds and dashboard queries executing in under 100 ms. PDAS directly resolves three critical gaps identified across three reviewed systems: the absence of live interactive dashboards, the lack of student-accessible analytics interfaces, and the failure to integrate data science processing into a production web application.