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UC Davis AI Course Recommender

Overview

This project is an AI-powered course recommendation system tailored for UC Davis students. It provides personalized course suggestions based on previously taken courses, course similarity, and degree requirements. The system also integrates a UI similar to UC Davis' Schedule Builder to enhance user experience.

Features Implemented

1. Course Recommender Based on Similar Courses

  • Uses cosine similarity to recommend courses similar to previously taken ones.
  • Extracts course descriptions and applies TF-IDF vectorization for semantic similarity.
  • Helps students explore courses aligned with their interests and academic goals.

2. UI Integration (Schedule Builder-like Interface)

  • Mimics the UC Davis Schedule Builder for intuitive course selection.
  • Displays recommended courses, major/core requirements, and GE courses.
  • Provides real-time unit tracking and waitlist status.
  • Integrates, MyDegree, MyBill, Academic History from OASIS All into ONE

Future Implementations

3. Professor Reviews and Ratings

  • Gather professor information from Rate My Professors or UC Davis sources.
  • Provide AI-generated insights on professor teaching styles, difficulty, and reviews.

4. Sample Schedule Generator

  • Generates a personalized course schedule based on:
    • Completed courses
    • Required major/core courses
    • Desired electives and interests
  • Optimizes schedule to balance workload and prerequisites.

Tech Stack

  • Python (Google Colab for development)
  • Pandas & NumPy (data handling)
  • Scikit-learn (cosine similarity, TF-IDF vectorization)
  • Flask/FastAPI (backend for UI integration)
  • React/Next.js (frontend for Schedule Builder UI)
  • Supabase (database management for storing user data)

How to Run

  1. Clone the repository: bash git clone https://github.com/yourusername/ucd-course-recommender.git cd ucd-course-recommender
  2. Install dependencies: bash pip install -r requirements.txt
  3. Run the AI Course Recommender in Google Colab:
    • Upload the dataset (course_catalog.csv).
    • Run the provided Colab notebook.
  4. Start the web interface: bash npm install npm run dev

Contributors

  • [Raquib Alam]
  • [Isaac Villegas]

Future Plans

  • Implement professor review analysis
  • Automate schedule generation with AI
  • Expand to include more universities

License

MIT License

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