sumeetk123/Dynamic-Pricing-Strategies-for-Retail-A-Data-Driven-Approach ? reverse-engineered prompt

Build me a notebook project that shows how to do dynamic pricing for retail using sales data. I want it to generate a larger sample dataset, clean and preprocess it, add useful features like season and product category, then train a few machine learning models to predict adjusted prices.

Please include the full workflow from reading the CSV data to saving a final adjusted prices file, and make it easy to see how promotions and seasonal trends affect pricing. I’d also like clear comparisons of model quality using metrics like MSE and MAE, plus a simple way to tune the best model and see which one performs the best.

Add a few helpful charts so I can understand the pricing changes and the seasonal patterns. If you need to check current notebook or library usage, look up the latest docs online. Keep it practical and easy to follow, since I want to use it as a data driven pricing demo.