ChemieTechie/fmcg-demand-forecasting-inventory-optimization ? reverse-engineered prompt

Build me a Jupyter notebook project for FMCG demand forecasting and inventory planning using the public Walmart M5 retail sales data.

I want it to load the sales, calendar, and price data, clean it, do some quick EDA on SKU store demand, and then compare a few forecasting methods for the next 28 days. Please include simple baselines, a seasonal approach, a moving average style model, a smoothing model, and a machine learning model using lag and calendar features. After that, evaluate the forecasts with common error metrics and show which approach works best on a small representative set of SKU store series.

Then take the forecast and turn it into practical inventory advice, like lead time demand, safety stock, and reorder point, using clear assumptions since the dataset does not include real inventory levels or supplier lead times. Keep the notebook readable and business focused, with charts, tables, and short explanations. If you need to check current library docs online, go ahead.