The main character energy for demand forecasting 🎬The algorithm ATE and left no crumbs 🍽️ Watch 295K sales records get sorted into the sickest price archetypes you've ever seen 👀

The first supply chain decision engine designed for the age of AI agents — graph-based, deterministic, explainable, API-first, open source

Machine learning project for retail demand forecasting and inventory planning using time-based validation, lag features, rolling-window features, and business error analysis.

A production-grade machine learning system for forecasting weekly retail store sales using real-world business data. Includes automated pipeline, business analytics, explainable AI, and an interactive forecasting dashboard.

Time series analysis and sales forecasting of Walmart store data using structured analytical methods. The project includes demand trend analysis, holiday vs non-holiday comparisons, and store-level moving average analysis to support inventory and staffing decisions.

Excel + Power Query inventory planning model: ABC/XYZ segmentation, safety stock, reorder point, replenishment logic

LightGBM demand forecasting system trained on 5 years of store-item sales data — predicts 3 months of daily demand using lag features, rolling averages, and seasonality signals.

Demand forecasting and inventory planning analysis for Horizon Hobby's new product launch using attachment rate methodology.

Model supply chain graphs, run MRP and shortage analysis, and expose planning data through a FastAPI REST API