A Decision Support System for Student Placement Prediction and Early Career Guidance

Abstract

Due to an increase in number of applications and intense competition in the campus placements, assessment of students’ preparedness for placements and their appropriate career choices, has become absolutely essential. This project aims at developing a decision support system based on machine learning algorithms to predict students’ placements and career planning. Through pertinent parameters such as cumulative grades, Communication skills, Coding abilities, Projects, Training Program attended, CGPA and Backlogs, students’ employability skills are identified. This approach estimates the possible salary, probability of getting placed, make use of classification and regression techniques of Random Forest algorithm, and also suggest the companies best suited to recruit. With skill gap analysis and suggestion by the system, students will be able to development action plans and be well prepared for campus placement. There are 3 modules, the admin dashboard for placement check, batch predictions for a set of students and a single student prediction for an individual, configured with the proposed system. The system can be run as an web application, constructed using the Streamlit framework, which presents instant visualisation, prediction, reporting, and decision support. The trial results show that the Random Forest algorithm has generated encouraging results in predicting students’ placements.