The chart drops. What actually happened?
Imagine Monday’s report: sales are lower than a week ago. A model can help outline questions, prepare calculations or describe differences. Before looking for a cause, though, establish whether you are comparing complete periods and the same data. Perhaps an export from one store simply has not arrived yet? It is an example, but it shows where sensible analysis begins.
Where AI can help
Start with a question you can answer
Take one report and one question, such as: which products explain the sales decline between two complete weeks? Define “sales,” the date range and how returns are counted. Calculate the result in a spreadsheet or database first, then ask AI to help describe it. If the model will forecast, reserve a separate period for evaluation and do not show that data during training. Using test-period data while training or preparing the model is data leakage, described in scikit-learn’s documentation. A small project like this shows where the model’s help ends and checking assumptions begins.