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>
14 lines
755 B
Markdown
14 lines
755 B
Markdown
# LinkedIn draft
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I built a privacy-safe demand-forecasting case study to show how I approach AI delivery as a player/coach.
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The demo starts with synthetic monthly demand by SKU and region, then compares:
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- an auditable baseline based on the previous eight months;
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- one gradient-boosting model with lag and calendar features;
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- rolling time-based validation instead of a random train/test split.
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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.
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The public repository contains no company data or private implementation details.
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