International Journal of Scientific Research and Engineering Development

The college admission process is often challenging for students due to the large amount of cutoff data, multiple admission categories, and lack of proper guidance during counseling. Students usually analyze previous year cutoff lists to estimate their chances of admission, but manual analysis can be difficult and inaccurate. This paper presents Campus Vision AI, an intelligent college admission prediction and counseling system developed using machine learning techniques. The system analyzes historical admission cutoff datasets and predicts the probability of admission into different colleges and branches based on student marks, category, and previous admission trends. A Random Forest machine learning algorithm is used to train the prediction model and generate reliable admission predictions. The system also integrates an AI-based counseling assistant that helps students understand their admission possibilities and generate optimized college preference lists. By combining machine learning and intelligent counseling support, the proposed system simplifies the admission decision process and provides data-driven guidance to students during the college counseling process.

Shifa Bilal Tamboli, Simeen Phiroj Mulani, Arman Tajuddin Shiakh,"Campus Vision AI: An Smart College Prediction and Counselling System" International Journal of Scientific Research and Engineering Development, V9(2): Page(2090-2093) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.