Gradvisor
A graduate school recommendation system that takes a student's academic profile and preferences as input and returns a ranked list of universities that are a strong fit. Built to cut through the noise of the graduate school search process and give students data-driven, personalized suggestions.
Profile-Based Matching
Students enter GRE scores, GPA, research interests, location preferences, and budget constraints. The system uses these as inputs to a weighted scoring model.
Scoring Algorithm
Universities are ranked based on a multi-factor weighted score that balances academic fit, financial feasibility, and program specialization — not just rankings.
Django REST Backend
Python/Django backend handles scoring logic, database queries, and API responses. SQLite stores university data, historical admissions stats, and program details.
Comparative View
Side-by-side comparison of recommended programs across key dimensions — tuition, program strength, location, and admission competitiveness.
How It Works
- ▸Student fills out a structured profile form with academic and personal preferences
- ▸Django backend processes inputs through a configurable weighted scoring function
- ▸Each university in the database is scored and ranked against the student profile
- ▸Frontend displays ranked results with match reasoning and comparison tools