BigMart Product Sales Factors by Kaggle | Finance and Economics

About this Dataset

BigMart Product Sales Factors

Investigating the Influence of Attributes and Store Characteristics

About this dataset

This dataset presents a unique opportunity for data scientists to uncover the real factors that drive product sales. By exploring this data, we can identify and evaluate the impact of product attributes and store characteristics that influence sales. By analyzing weight, fat content, visibility, item types, maximum retail price (MRP), outlet size, location type and type of outlet features on sales data of 1559 products across 10 stores in different cities - all collected in 2013 by BigMart - we can create models that accurately predict product sales volumes. This dataset encourages us to dig deep and analyze how individual characteristics like item weight or size or visibility impact our ability to understand store performance measures like market share or average basket values. Finally it allows us to unpack the link between retailer strategies like promotions or deals with in-store success; giving us a true picture of what makes for successful products at different stores within different markets

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How to use the dataset

This guide is intended to provide a helpful overview for those interested in exploring the BigMart Product Sales Factors dataset. This dataset contains sales data of 1559 products across 10 stores in different cities collected in 2013 by data scientists at BigMart. This dataset presents an exciting opportunity to explore the impact of product attributes and store characteristics on the sales of products from the way products are perceived and presented in stores to the location and outlet type.

Research Ideas

Using the data to predict the optimal store set-up that would maximize product sales. By understanding the effects of product attributes, store location and outlet type, one could determine what type of products should be stocked in which particular stores in order to increase sales.

Utilize data analysis to examine customer buying preferences and identify what items are most popular among different cities or areas based on demographic characteristics like income or age group. This would allow for targeted prediction models for pricing, promotion and inventory stocking decisions related to those products

Take advantage of this data to optimize marketing strategies by determining when discounted items are more likely to sell better than full-price items, cultural differences in purchasing preferences between cities or predict future trends based on historic sales patterns using Machine Learning algorithms like Reinforcement Learning or Decision Trees

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: data.csv

Column name	Description
Item_Weight	Weight of the product in kilograms. (Numeric)
Item_Fat_Content	The fat content of the product. (Categorical)
Item_Visibility	The visibility of the product in store or online. (Numeric)
Item_Type	The type of product, such as limited offers or no offer. (Categorical)
Item_MRP	The maximum retail price of the product. (Numeric)
Outlet_Establishment_Year	The year the outlet was established. (Numeric)
Outlet_Size	The size of the outlet, either retail or supermarket. (Categorical)
Outlet_Location_Type	The type of location of the outlet, such as urban or rural area. (Categorical)
Outlet_Type	The type of outlet, such as sales departmental store or supermarket. (Categorical)
Item_Outlet_Sales	The sales of the product in the outlet. (Numeric)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Data

@kaggle.thedevastator_bigmart_product_sales_factors.data

374.1 KB
14204 rows
12 columns


CREATE TABLE data (
  "item_identifier" VARCHAR,
  "item_weight" DOUBLE,
  "item_fat_content" VARCHAR,
  "item_visibility" DOUBLE,
  "item_type" VARCHAR,
  "item_mrp" DOUBLE,
  "outlet_identifier" VARCHAR,
  "outlet_establishment_year" BIGINT,
  "outlet_size" VARCHAR,
  "outlet_location_type" VARCHAR,
  "outlet_type" VARCHAR,
  "item_outlet_sales" DOUBLE
);