Retail is another industry which extensively uses analytics to optimize business processes. Tasks like product placement, inventory management, customized offers, product bundling etc are being smartly handled using data science techniques. As the name suggests, this data comprises of transaction record of a sales store. This is a regression problem. The data has 8523 rows of 12 variables.
Problem: Predict the sales
Variable Description Item_Identifier Unique product ID Item_Weight Weight of product Item_Fat_Content Whether the product is low fat or not Item_Visibility The % of total display area of all products in a store allocated to the particular product Item_Type The category to which the product belongs Item_MRP Maximum Retail Price (list price) of the product Outlet_Identifier Unique store ID Outlet_Establishment_Year The year in which store was established Outlet_Size The size of the store in terms of ground area covered Outlet_Location_Type The type of city in which the store is located Outlet_Type Whether the outlet is just a grocery store or some sort of supermarket Item_Outlet_Sales Sales of the product in the particulat store. This is the outcome variable to be predicted.