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>

pyproject.toml

@@ -0,0 +1,16 @@
+[project]
+name = "ml-forecasting-case-study"
+version = "0.1.0"
+description = "Privacy-safe demand forecasting case study"
+requires-python = ">=3.11"
+dependencies = [
+  "jupyter",
+  "matplotlib",
+  "nbclient",
+  "numpy",
+  "pandas",
+  "scikit-learn",
+]
+
+[tool.uv]
+package = false