# 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.