Add privacy-safe demand forecasting case study

Synthetic monthly SKU/region demand notebook comparing a trimmed-mean
baseline against gradient boosting, with a gated TimesFM build-vs-buy
comparison. Includes rolling-origin validation, executive narrative,
architecture diagram, one-page PDF, and LinkedIn draft.

No company data, credentials, or private implementation details.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

docs/linkedin-post.md

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+# LinkedIn draft
+
+I built a privacy-safe demand-forecasting case study to show how I approach AI delivery as a player/coach.
+
+The demo starts with synthetic monthly demand by SKU and region, then compares:
+
+- an auditable baseline based on the previous eight months;
+- one gradient-boosting model with lag and calendar features;
+- rolling time-based validation instead of a random train/test split.
+
+The important part is not adding the most models. It is making the path from business question to reproducible evidence explicit: prepare the data, establish a baseline, validate the model, document the operating boundary, and leave a clean seam for future orchestration.
+
+The public repository contains no company data or private implementation details.