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Capstone project for Data Science and Machine Learning

Forecast demand to cut out-of-stocks

About 9% of Mangrove app orders lose an item because it is out of stock, and today's ordering rule (cover the next 4 days at the last 4 weeks' average) under-orders badly in festival weeks. Build a demand forecast for 50 fast-moving products in 20 stores and show how much it would reduce out-of-stocks compared with today's rule. Use a public grocery or retail sales dataset (for example the M5 competition data) or a synthetic Mangrove dataset you generate, and say which. Frame the problem, build leakage-free features, validate by time, compare at least two models with the baseline, look at error by product group, store band and festival week, turn the forecast into order quantities at a chosen percentile, and write a one-page summary the supply chain team could act on.

What you hand in

  1. A notebook with data preparation, a time-based backtest, a baseline and at least two models, with error tables
  2. An error analysis by product group, store size and festival week, with WAPE and bias
  3. An ordering simulation that compares out-of-stocks and stock held under today's rule and under your forecast
  4. A one-page summary for the supply chain team: the result, the limits, and a pilot proposal

Check your work

Tick each item when your project meets it. Be strict: this is the rubric the reviewer uses.

  • The problem is framed as a decision: target, unit, horizon, what is known at prediction time, a model metric and a business metric
  • Validation respects time order: training before validation with a gap for lead time, preprocessing fitted on training folds only, and no feature known only after prediction time
  • Every model is compared with the 28-day-average baseline and a seasonal naive forecast on the same folds and the same metric
  • Error and bias are reported per product group, store band and festival week, and the worst segment gets a stated fix or fallback
  • The forecast is turned into order quantities at a chosen percentile, with the out-of-stock and stock-held trade-off shown against today's rule
  • Stock-out days are handled as censored demand rather than used as normal labels
  • The summary states limits, risks and next steps in the supply chain team's language (units, stock-outs, waste, money)

0 of 7 checked

Model answer

The model answer opens once your capstone passes the review, so you can compare your work with an outline of a strong submission.

Hand in your capstone

Your links are checked automatically, then the work is scored against the rubric above, item by item, with comments. A pass counts toward the course certificate and shows on your profile.

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