DSN Pre-Bootcamp Hackathon: The Excellent Store Challenge by Data Science Nigeria
Predict profit returns per product per outlet
336 data scientists enrolled, 140 on the leaderboard
Customer serviceRetailPredictionStructured
8 August 2020—22 August 2020
15 days

You have been provided with transactional records of all the stores at product level. Due to power failure and technical glitches, some stores might not report all data, hence the data may have missing values.

Variable Description

  • Item_ID: Unique product ID
  • Item_Weight: Weight of the product
  • Item_Sugar_Content: Sugar content of the product
  • Item_Visibility: The percentage of total display area of all products in Chief Babatunji’s supermarket allocated to the particular product
  • Item_Type: The category to which the product belongs
  • Item_Price: Retail price of the product
  • Store_ID: Unique store ID
  • Store_Start_Year: The year in which store was opened
  • Store_Size: The size of the store in terms of total ground area covered
  • Store_Location_Type: The type of city in which the store is located
  • Store_Type: Description of the store based on category of items sold
  • Item_Store_ID: Unique identifier of each product type per supermarket.
  • Item_Store_Returns: Profit returns on the product in the particular store. This is the outcome variable to be predicted.