This project is to predict course grade distributions and popularity rankings for upcoming semesters, enabling students to make informed decisions about their class selections. By shifting the focus from individual grade predictions to overall course outcomes, the project provides insights into course grading trends and demand. It uses clustering to rank courses based on student performance and popularity, and topic-based grouping to help students discover courses aligned with their interests, factoring in professor expertise and class attributes. This data-driven tool uncovers hidden patterns, aiding both students and academic planning.
- Arlette Diaz (adiaz218)
- Marianne Hernandez (marhern19)
- Nandini Jirobe (nandinijirobe)
- Sharadruthi Muppidi (sharadruthi-uic)
- Sonina Mut (snina22)
- Yuting Lu (yutinglu103)
