{"repo":"AmirhosseinHonardoust/Cafe-Demand-Forecasting-Reorder-Simulator","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Cafe-Demand-Forecasting-Reorder-Simulator","clone":"git clone https://github.com/AmirhosseinHonardoust/Cafe-Demand-Forecasting-Reorder-Simulator.git","description":"End-to-end café inventory project: clean transaction data, build daily item-level demand series, backtest strong baseline forecasters, generate next-30-day demand forecasts, convert forecasts into safety stock + reorder points, and validate policies with Monte Carlo stockout-risk simulations, wrapped in a Streamlit dashboard.","language":"Python","stars":10,"topics":["backtesting","baseline-models","dashboard","data-analysis","data-science","decision-science","demand-forecasting","forecasting","inventory-management","kaggle-dataset"],"license":"MIT","category":"trading","readme_excerpt":"Café Demand Forecasting + Inventory Reorder Simulator A portfolio-grade analytics project that turns café transactions into (1) item-level demand forecasts and (2) an inventory reorder policy (Reorder Point + Safety Stock), then validates the policy with a Monte Carlo stockout-risk simulation . This repo is built around a real decision: “For each item, when should I reorder (ROP), how much should I target (Order-up-to), and what stockout risk am I accepting?” --- Dataset (Source + Thanks) This project uses the Kaggle dataset: - Cafe Sales Dataset (Clean) by aramelheni - Dataset URL: https://www.kaggle.com/datasets/aramelheni/cafe-sales-dataset-clean Thanks to the author and Kaggle for making the data publicly available. --- Table of contents - What this project delivers - Dashboard tour (with screenshots) - How it works (end-to-end) - Figures explained (analysis outputs) - Quickstart - CLI options - Outputs - Project structure - Decision safety + limitations - Next upgrades --- What this project delivers 1) Clean, decision-ready daily demand per item Raw data is transaction-level (each row = one purchase event). Inventory decisions need a daily signal. This repo creates: - Daily demand (units) per item - Daily revenue per item - Zero-filled missing days so each item becomes a continuous time series (no “holes”) 2) Forecasting you can explain (and defend) Instead of jumping to complex models, we start with strong baselines that many real businesses use because they’re: - simpl","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Cafe-Demand-Forecasting-Reorder-Simulator/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}