WashU Meal Plan Optimizer (based on 2026-27 meal point policy)

Predict your next-semester WashU Dining spending from local uploads, then choose the cheapest practical meal plan instead of overbuying points.

Assumption: if a student starts living off campus, the model assumes they will not move back to a dorm later in the academic year.

1) Choose Prediction Mode

If you are going to deside your plan for fall semester, choose fall mode; if you are going to deside your plan for spring semester, choose spring mode.

2) Spring Student Context

Spring mode keeps its own student context and optional rollover inputs.

Required spring inputs

Meal transactions CSV only. On-campus students use the full-day campus assumption.

3) Spring Inputs

Manual on-campus time replaces schedule file uploads.

Living on campus is set to Yes, so schedule input is skipped and the model assumes full-day campus presence.

Fix the spring input issues below to enable analysis.

  • - Meal transactions CSV: no file selected

4) Spring Results

Prediction output and plan recommendation dashboard for spring mode.

Add your meal CSV and required on-campus time inputs, then run analysis to view results.

5) Methodology & Caveats

  • - Deterministic local heuristics classify spending into lunch/snack/dinner/misc categories.
  • - Off-campus users enter manual on-campus time blocks instead of uploading schedules.
  • - A modest shortfall is acceptable and often cheaper than buying a higher plan.
  • - For spring, fall rollover is included; on-campus users use a full-day campus assumption.
  • - “Suggested Balance” is intentionally ignored in recommendation logic.

6) Privacy

All processing runs locally in your browser. No login, no backend database, and no server-side storage. Uploaded files are not persisted; refreshing the page clears all loaded data.

7) Support The Project

If you found this useful, please give the project a star on GitHub.