UniMe
An AI-assisted matching experience spanning 1,000+ programs, secure student profiles, and downloadable admission reports.
Try the live projectCarry-on evidence
- 1K+
- Programs analyzed
- 200+
- Students served
- Takeaway
01 / Context
A long program list is not yet a useful choice.
University research asks students to compare a large number of programs against goals that are personal and difficult to rank.
UniMe turns a structured student profile into a smaller set of program matches and a result that can be kept.
02 / Matching engine
A custom score makes different programs comparable.
The recommendation engine analyzes more than 1,000 university programs with custom scoring algorithms.
Pandas processes structured student and program data before the results are returned through the Flask API.
03 / Full-stack system
The recommendation needs a secure path to the screen.
The Flask REST API uses JWT authentication and PostgreSQL, while React and TypeScript validate the student-facing form.
That end-to-end path has served more than 200 students with personalized results.
04 / Reflection
A recommendation is stronger when it can be revisited.
UniMe produces automated PDF admission reports rather than ending the experience at a transient result screen.
The project connects model logic, secure data handling, and a tangible decision aid.
Turning paginated Meta Careers listings into structured, reusable data.
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