Inspiration
To assist advisors with a tool that can easily suggest courses fit for them
What it does
Uses past student's records to suggest helpful courses for them by comparing compatibility with teaching styles and learning styles
How we built it
Used Flask and the provided scripts to create a database to draw a knowledge graph of students, faculty, courses, and their relationships such as student similarity and completed courses.
Challenges we ran into
Importing the database was a challenge since it took so much time before we realized we should've imported a smaller dataset. It was also a challenge implementing the Gemini API into our application. The running time of our application is also a little bit slow.
Accomplishments that we're proud of
Building a clean UI and succesfully integrating both neo4j and Gemini API into our project.
What we learned
We learned how to use neo4j and the Gemini API. Also learned to connect neo4j into Flask.
What's next for PawPrint
Implementing a student friendly interface since right now it is very geared towards advisors since you can view all students.
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