930534dde2
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>
17 lines
279 B
TOML
17 lines
279 B
TOML
[project]
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name = "ml-forecasting-case-study"
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version = "0.1.0"
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description = "Privacy-safe demand forecasting case study"
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requires-python = ">=3.11"
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dependencies = [
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"jupyter",
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"matplotlib",
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"nbclient",
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"numpy",
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"pandas",
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"scikit-learn",
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]
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[tool.uv]
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package = false
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