DataMunglers

Team Members

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. The main objective is to understand whether specific properties of products and/or stores play a significant role in terms of increasing or decreasing sales volume. To achieve this goal, we will build a predictive model and find out the sales of each product at a particular store. This will help BigMart to boost their sales by learning optimised product organization inside stores.

We take data from analyticsvidhya.com, Our data is Big Mart Sales Practise Problem Data, It is open comppetition now and its final date is 31 Dec 2017. We downloaded training dataset as a csv file.

Big Mart is One Stop Shopping center and Free Marketplace. Buy, sell and advertise without fee or at low cost. Find more information about BigMart Here.

We have train (8523) and test (5681) data set, train data set has both input and output variable(s).
Item_Identifier : Unique Product ID
Item_Fat_Content : Whether the product is low fat or not
Item_Type : The category to which the product belongs
Outlet_Identifier : Unique Store ID
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_Weight : Weight of product
Item_Visibility : The % of total display area of all products in a store allocated to the particular product
Item_MRP : Maximum Retail Price (list price) of the product
Outlet_Establishment_Year : The year in which store was established
Item_Outlet_Sales : Sales of the product in the particular store. This is the outcome variable to be predicted.

Note: You can find detailed data explanations here.