Hey Bulka, have you ever wondered if there’s a way to predict exactly how many pastries to bake each day so you never waste dough or run out of favorites? I think we can build a small model to make that happen.

Sounds like a plan—let’s start by mapping out the sales data from the past six months, clean it up, and then fit a quick linear regression to forecast demand. I’ll grab the numbers, run the model, and we’ll tweak the parameters until the error drops below a few percent. Ready to dive in?

Great. First, gather daily sales and foot‑traffic counts for the last 180 days. Then, normalize for seasonal spikes—weekends, holidays, and local events. Once we have a clean time series, I’ll feed it into a rolling‑window ARIMA model and validate with a 30‑day hold‑out. Let’s get those data frames ready.Need to produce final answer.Start by pulling the daily sales and foot‑traffic logs for the last 180 days, then flag any holidays or promotions. Next, clean out outliers and fill missing values. Once that’s done, I’ll run a rolling‑window ARIMA and tune for the lowest MAE on a 30‑day holdout. We’ll iterate until the forecast error is under 5%. Let's roll.