Author Affiliations
[1] Assistant Professor, Department of CSE, Er. Perumal Manimekalai College of Engineering, Hosur-635117, Anna University.
[2] [3] [4] Student, Department of CSE, Er. Perumal Manimekalai College of Engineering, Hosur-635117, Anna University, Tamil Nadu.
Abstract
This paper presents PlaceIQ, a web-based Student Placement Intelligence Platform that leverages data analytics and machine learning to predict student placement outcomes. The system ingests structured academic records (CGPA, skills, internships, projects, and arrears) and applies a configurable weighted scoring engine to classify students into placement tiers (A–D) and estimate employability probability. Implemented as a zero-backend, client-side single-page application (SPA) using HTML5, CSS3, and vanilla JavaScript, PlaceIQ incorporates Chart.js for real-time interactive dashboards and PapaParse for high-performance CSV processing. Evaluation on 500 anonymised student records across three graduation cohorts yields a prediction accuracy of 91.4% and an F1-score of 0.90, outperforming Logistic Regression, Decision Tree, and Random Forest baselines. The platform delivers multi-dimensional analytics including placement rate trends, CGPA distribution histograms, skills gap analysis, and company-wise eligibility breakdowns, enabling placement officers and students to make informed, evidence-based decisions without requiring any server infrastructure.
Keywords: placement prediction; machine learning; data analytics; student employability; weighted scoring; decision support system
How to Cite This Article
Charumathi R, Bindhu K, Aishwarya D, Gayathri Devi G S (2026). Placement Prediction System Using Data Analytics and Machine Learning. International Journal of Innovative Research in Multidisciplinary Education & Technology (IJIRMET), 11(3).