This project focuses on analyzing Big Mart's sales data to uncover insights and support data-driven decision-making. By leveraging SQL, I have executed a series of queries to perform various data manipulation tasks. SQL's powerful capabilities enable efficient data management and manipulation, allowing us to derive meaningful insights from the data.
- 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.