Student Placement and Job Role Predictions

Abstract: Career selection remains a critical challenge for students, influenced by external pressures and insufficient guidance. This study proposes a machine learning-based job role prediction system to address career uncertainty. Leveraging algorithms like Random Forest, Decision Tree, and SVM, the model analyzes academic performance, skills, interests, and personality traits from a dataset of 20,000 records. Data preprocessing included handling missing values, categorical encoding, and feature scaling, while Recursive Feature Elimination identified key predictors. Hyperparameter tuning via Grid Search optimized the Random Forest model, achieving 80% accuracy. A dynamic quiz integrated into the system assesses logical reasoning, reducing self-reporting bias. Implemented as a web tool using Django and Tailwind CSS, the platform offers personalized career recommendations, particularly for computer science students. Results highlight the efficacy of multi-dimensional analysis in reducing career indecision. Future work may explore ensemble methods and real-time skill gap analysis to enhance predictions