I hope that some of you are familiar with Big Mart sales prediction data that was provided by Analytics Vidhya as a contest. The problem statement of on the website is as follows:

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

My question is in which way this is considered as a regression problem? We do not have historical time-series sales data to predict the future ones. What we actually have is the annual sales of some products (the target variable) based on the other 11 attributes describing the product (for example the fat content, weight, visibility...) and the outlet (Outlet type, size, location...). In which sense are we solving a regression based prediction problem here?

  • $\begingroup$ The problem is clearly stated in the penultimate paragraph. The ultimate paragraph does not seem to reflect this statement: instead, it veers from the concern to "understand properties" to different questions of prediction from time series. In this sense your question has no foundation and risks being misinterpreted. $\endgroup$ – whuber Jul 1 at 13:33
  • $\begingroup$ To understand properties is an important goal but this is to be done through a predictive model that is where my prediction related concern come from. $\endgroup$ – user233531 Jul 1 at 13:37
  • $\begingroup$ Perhaps the people stating that the aim is "to build a predictive model" are using "predictive model" as a synonym for "regression model." $\endgroup$ – whuber Jul 1 at 13:56
  • $\begingroup$ That is part of my question, how a regression model is interpreted in such a case? $\endgroup$ – user233531 Jul 1 at 14:02

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