COLLEGE PREDICTION SYSTEM

Abstract

The journey towards higher education is a pivotal chapter in a student's life, often marked by uncertainty and high stakes. In the context of engineering admissions in Maharashtra, where over 400 colleges are governed by the Directorate of Technical Education (DTE), the process can be labyrinthine. The Centralised Admission Process (CAP) involving multiple rounds further amplifies the complexity, leaving students perplexed about selecting the ideal college for their academic aspirations. The College Admission Prediction System is an innovative web application designed to address these challenges and bring clarity to the admission process. Leveraging the power of machine learning, the system employs the Adaboost algorithm and data analysis techniques to empower students with accurate and data-driven insights. By considering factors like rank, state domicile, category, preferred branches, preferred districts, and preferred colleges, the system generates a preference list based on a thorough analysis of three years' historical cutoff data. This prediction model is a valuable tool, enabling students to make well-informed decisions prior to seat allotment in both the first and second CAP rounds. The project is driven by a vision to alleviate the stress and insecurity associated with college admissions. By offering students transparent and reliable predictions, it transforms the admission process into a more navigable and data-guided journey. The College Admission Prediction System not only benefits students by aiding in college selection but also supports educational institutions in optimising seat allocation. This project signifies a significant step towards data-driven decision- making in the education sector and promises to enhance the overall experience of students pursuing engineering education in Maharashtra. Keywords: Admission, prediction model, adaboost algorithm, data analysis.