SUBMISSION: Milestone Project 1 — Problem Formulation: Framing a Real Business Problem as an ML Task

# SUBMISSION: Milestone Project 1 — Problem Formulation: Framing a Real Business Problem as an ML Task > 🎯 **Hands-on Portfolio Deliverable.** Before any model: the highest-leverage ML skill is turning *"churn is up, do something"* into a solvable, measurable task. This brief is the artifact you will show for *"tell me how you scope an ML problem."* ## 🏢 Business Scenario & Executive Prompt You are the first ML hire at **DailyKart**, a D2C grocery subscription service (400K subscribers). The CEO says: *"Churn jumped from 6% to 9% this quarter. Build a churn model."* The retention team can call 5,000 customers/month. Nothing else is defined — no label, no metric, no baseline. Your job: produce the task definition a team can execute without rework. Ambiguity you leave here becomes leakage and wasted modeling later. ## 📋 Source Data & Input Schemas **`dailykart_events.csv`** — 2.1M rows (mock shape; generate with the scaffold): | column | dtype | description | |---|---|---| | `customer_id` | str | unique subscriber (`C-000001` …) | | `event_ts` | datetime | event timestamp (IST) | | `event_type` | category | `order \| skip \| pause \| support_ticket \| payment_failed` | | `basket_value_inr` | float | order value (> 0 for orders) | | `plan` | category | `weekly \| monthly` | | `acquired_on` | date | subscription start date | **`customers.csv`** — 400K rows: `customer_id, acquired_on, city_tier, acquisition_channel`. Expected shapes: median tenure 7 months; ~9% of customers lapse (no order in 45 days) in the latest quarter. ## 🎯 Required Deliverables 1. **`problem_framing.md`** — label definition, observation/prediction windows, success metric + why, baseline to beat, risks. 2. **`labels.csv`** — `customer_id, observation_end, label (0/1)` for one cohort (≥ 50K rows), built by your code. 3. **`baseline.ipynb`** — runnable notebook: cohort build, dummy/majority baseline scores, metric table. 4. **`metric_memo.md`** — one page: why accuracy is rejected, chosen operating metric, cost math for the 5,000-call capacity. ## 🛠️ Step-by-Step Implementation Guide 1. **Define the label.** Pick an observable lapse event (e.g. no order within 45 days after observation end). Justify the horizon from business cost, not convenience. 2. **Fix the windows.** Observation window (features allowed) vs prediction window (label). Draw the timeline diagram; state the as-of rule in one sentence. 3. **Leakage-proof the cohort.** Every feature timestamp must be ≤ observation_end. Write an assertion that fails the build if violated. 4. **Choose the metric.** Reject accuracy with the 91%-majority math; select precision@K / recall@K tied to 5,000 calls/month and cost per call vs save value. 5. **Set the baseline.** Majority-class and rule-based (e.g. "payment_failed recently") baselines with the chosen metric — the bar every later model must clear. 6. **Write the memo.** One page a PM can sign: what you predict, when, how success is measured, what could invalidate it. ## 💻 Complete Runnable Starter Scaffold ```python import pandas as pd OBS_END = "2026-04-30" # as-of point: NOTHING after this may enter features PRED_WINDOW_DAYS = 45 events = pd.read_csv("dailykart_events.csv", parse_dates=["event_ts"]) ev = events[events["event_ts"] OBS_END) & (events["event_type"] == "order")] lapsed_ids = set(ev["customer_id"]) - set(future_orders["customer_id"]) labels = pd.DataFrame({"customer_id": sorted(set(ev["customer_id"]))}) labels["observation_end"] = OBS_END labels["label"] = labels["customer_id"].isin(lapsed_ids).astype(int) assert (pd.to_datetime(labels["observation_end"]) >= ev.groupby("customer_id")["event_ts"].max().reindex(labels["customer_id"]).values).all() labels.to_csv("labels.csv", index=False) print(labels["label"].mean(), len(labels)) # expect ~0.09, >= 50000 rows ``` ## 🧪 Acceptance Criteria & Verification Checks - [ ] `labels.csv` has ≥ 50K rows with a documented lapse rate near 9%. - [ ] Build script asserts all feature timestamps ≤ observation_end (leakage guard runs green). - [ ] `metric_memo.md` rejects accuracy with majority-class math and ties the metric to the 5,000-call capacity. - [ ] Baseline notebook runs top-to-bottom and reports dummy + rule-based scores on the chosen metric. ## 📊 Evaluation Rubric (100 points — passing ≥ 80) | # | Criterion | Points | What earns full marks | |---|---|---|---| | 1 | Modeling correctness | 25 | Right algorithm for the task; leakage-free splits/pipelines; metrics match the business decision; numbers reproducible | | 2 | Code quality & reproducibility | 25 | Seeded RNG, re-runnable notebook/scripts, typed schemas, no hardcoded paths or magic numbers | | 3 | Business reasoning & memo | 25 | Decision-oriented write-up with costed trade-offs; honest about uncertainty, limits, and risks | | 4 | Visualiza