School Visit Tracker

An automated monthly (November) classroom-observation visit scheduler for 500 partner schools in Tripura, covered by a 120-member field team. Built for Labhya's Data Engineer prework (Question 2).

Given each school's assigned visit frequency and operating calendar, the scheduler produces a day-by-day schedule showing which field member visits which school, while respecting all stated constraints (frequency, one-visit-per-member-per-day, weekday rotation, dedicated vs. flexible member pools).

Repo structure

dataset/    Source CSVs (schools + team members) — the given input data
plan/       Requirements analysis and algorithm design docs
output/     Generated schedule CSVs (produced by scheduler.py)
scheduler.py  The scheduling script

• plan/REQUIREMENTS.md — data structure, requirements, resolved assumptions/conflicts, and feasibility analysis.
• plan/ALGORITHM_PLAN.md — the step-by-step scheduling algorithm design, plus known limitations.
• scheduler.py — reads the two dataset CSVs and generates the schedule.
• output/schools_schedule.csv — school × day grid; V marks a scheduled visit, - marks a closed day (Sunday for all, Saturday for Vidya Jyoti schools).
• output/team_members_schedule.csv — member × day grid; each cell holds the school code the member visits that day (this is the sheet a field team member reads to plan their day).

Running it

python3 scheduler.py

Requires only the Python standard library (no dependencies). Re-running regenerates both files in output/ and prints a validation summary (visit-count checks, closed-day checks, weekday-rotation checks).

Key assumptions

• Reference calendar: a hypothetical month where Nov 1 falls on a Monday (see plan/REQUIREMENTS.md for details).
• No location/geography data exists in the source CSVs, so flexible field members are assigned to schools deterministically (by index), not by travel distance.
• Idle days for flexible members are allowed — the schedule doesn't manufacture filler visits just to fill every day.

Full rationale for these and other decisions is in plan/REQUIREMENTS.md and plan/ALGORITHM_PLAN.md.