AI-Based Mock Interview, Aptitude and Placement Prediction Platform for Engineering Students

Campus placement outcomes for engineering graduates depend on aptitude, technical depth, communication and academic standing, yet most students prepare without structured, personalised feedback. This paper presents an integrated web platform that combines (i) a rule-based adaptive aptitude engine that adjusts question difficulty from recent accuracy and response time, (ii) an AI- assisted mock interview module that accepts typed and live voice responses and scores them across multiple competency dimensions, (iii) a machine-learning placement-readiness predictor, and (iv) a personalised 7/14/30-day preparation planner. The system is implemented with React, FastAPI, SQLAlchemy and scikit-learn, together with a separate Express service that sends voice-interview transcripts to a large language model and falls back to a heuristic scorer when the model is unavailable. On a 1,000-record synthetic dataset, a logistic- regression model reached 94.5% accuracy, a 96.2% F1-score and 0.985 ROC-AUC on a held-out 20% split, outperforming a random- forest baseline (91.5% accuracy). Because the labels are synthetic, these figures validate the machine-learning pipeline rather than real- world predictive power; validation on institutional placement records is identified as the principal next step.