Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: tabular-regression
+tags:
+- tubular-regression
+- demand-forecasting
+---
+
+# Shelfy: Tomorrow's Best Sellers
+**AI Demand Prediction** - Rank products by tomorrow's conversion rate. Top 30% products captures **50% of actual sales**.
+
+## Usage
+1. Enter today's product stats
+2. See tomorrow's predicted conversion + revenue
+3. **Stock Top 10% products → 2.5x ROI**
+   
+## Business Results (42M events test)
+- Top 10% → **25.4%** sales - ROI: **2.5x** <br>
+- Top 30% → **52.1%** sales - ROI: **1.7x** <br>
+- **Test:** Oct 25-29 (blind)  **Dataset:** 42M ecommerce events
+
+## Features
+- **Live prediction** - Tomorrow's conversion rate per product
+- **Demand signals** - High / Medium / Low
+- **production features** - Views, carts, momentum, intent ratios
+
+## Model Card
+- Architecture: XGBoost Regressor
+- Features (8): views(29%) + carts(29%) + cart momentum(13%) + user metrics(17%) + other(2%)
+- Target: Tomorrow's purchase rate (actual purchases / views)
+- Stability: 82% day-to-day Top 10% products overlap
+
+## Production Stack
+- Data: Clickstream (views, carts, purchases)
+- Stack: Python + XGBoost + FastAPI + PostgreSQL
+- Retraining: Weekly with shelfy's real data
+
+## Tech Stack
+- Model: shelfy_purchase_rate_v2.joblib (XGBoost)
+- UI: Gradio 4.44.0
+- Data: Ecommerce behavior (Oct 2019)
+
+---
+**Built for datastorm hackathon** | **Apache 2.0** | **Deployed 1/16/2026**