PickMyProf

Inspiration

The process of registration and the pressure to perform when taking chances with professors and their supposed track record inspired us to build this project.

What it does

The project takes real world feedback from multiple sources in order to determine the quality of learning and degree of satisfaction of the many courses that UTD has to offer. PickMyProf can be utilized by the UTD community to improve their decision making process while choosing professors.

How we built it

Front end using MUI bootstrap template which we added redux to connect it to our API

Challenges we ran into

The integration between multiple platforms was one of our biggest challenges. Ensuring that the various languages, user interfaces, backend development and front end implementation coincided with each other was an immense task.

Accomplishments that we're proud of

The NLP ML/AI model which achieves semantic analysis of real world reviews as seen on reddit. Creating a tableau dashboard visualization which accurately depicts the way past students have experienced the respective courses GCP implementation for hosting the API - PickMyProf

What we learned

Real world deployment of cloud based software for AI/ML or NLP analysis. The integration of tableau into React was more complicated than we had anticipated.

What's next for PickMyProf

Utilizing the live tableau servers in order to provide real time integration of interactive dashboard visualization, Optimize cloud implementation and extend it to multiple cloud platforms for maximum usability, Further refine the AI/ML model for NLP so that it may more accurately understand human emotion based off of time, date, tone, language and urgency of the posted review Consider multiple sources of review and data to enhance professor ratings, improve semantics to further understand human emotions as numeric data and analysis.

Built With

• .net
• c#
• cohere
• gcp
• machine-learning
• mui
• natural-language-processing
• node.js
• react
• semantic
• tableau