This problem is taken from an old Kaggle Competition from 2018. The competition's description details:
Brick-and-mortar grocery stores are always in a delicate dance with purchasing and sales forecasting. Predict a little over, and grocers are stuck with overstocked, perishable goods. Guess a little under, and popular items quickly sell out, leaving money on the table and customers fuming.
The problem becomes more complex as retailers add new locations with unique needs, new products, ever transitioning seasonal tastes, and unpredictable product marketing. Corporación Favorita, a large Ecuadorian-based grocery retailer, knows this all too well. They operate hundreds of supermarkets, with over 200,000 different products on their shelves.
Corporación Favorita has challenged the Kaggle community to build a model that more accurately forecasts product sales. They currently rely on subjective forecasting methods with very little data to back them up and very little automation to execute plans. They’re excited to see how machine learning could better ensure they please customers by having just enough of the right products at the right time.
The Kaggle Competition, including all of the data, can be found here.
For this problem, you should put yourself in the position of a Data Scientist working at Corporación Favorita.
Although the competition only asks for a model, your solution should also detail:
Additionally, here are some questions that would be interesting to answer: