Smart Campus Match

Selecting an appropriate college and academic branch is a critical challenge for students due to the availability of large and complex admission data. This paper presents a hybrid college recommendation system that integrates content-based filtering with collaborative filtering to provide personalized and realistic recommendations. The system analyzes student-specific inputs such as entrance rank and location to generate ranked college suggestions. Implemented as a web-based application using Python and Flask, the proposed system improves decision-making efficiency and reduces information overload, making it a practical decision-support tool for students