Dark Mode
Retail Product Sales Drivers
Retail & Consumer Behavior
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Focuses on predicting sales for products across various outlets within a large retail chain environment. The challenge utilizes this dataset to encourage the data science and machine learning community to construct models capable of accurately forecasting the sales volume of individual products from specific retail outlets. Analysing sales data is vital for ensuring the financial sustainability of these marts, especially given the market competition and customer focus on product quality and accurate pricing. Users are encouraged to analyse the intrinsic properties of the products and the attributes of the stores to identify effective ways to increase overall sales performance.
Columns
- Item_ID: The unique identification number assigned to the product.
- Item_W: The physical weight of the item.
- Item_Type: A categorical description of the item.
- Item_MRP: The Maximum Retail Price set for the product.
- Outlet_ID: The identification code for the specific retail location.
- Outlet_Year: The year that the outlet was established.
- Outlet_Size: Classification detailing the physical size of the outlet (e.g., small, medium, large).
- Outlet_Type: Classification detailing the type of retail location (e.g., supermarket, grocery).
- Sales: The total sales revenue generated from the outlet for that specific item.
- Predicted Outlet Sales: The variable intended for prediction, relevant for the ML challenge submission.
Distribution
This dataset typically resides in a tabular format, such as CSV files. It contains a collection of records relating item characteristics to outlet properties and corresponding sales figures. The data is suitable for structured analysis and modelling. While specific record counts are not detailed for the main file, related submission files indicate a valid label count of approximately 37.7 thousand records. The expected update frequency for this collection is listed as never.
Usage
This data is perfectly suited for developing robust machine learning models focused on sales prediction and demand forecasting. Key use cases involve applying advanced feature engineering techniques, conducting deep business intelligence analyses to understand sales drivers, and participating in forecasting competitions designed to optimise retail strategies.
Coverage
The dataset focuses on attributes of retail outlets, including their establishment year and size, alongside product-specific details such as weight and maximum retail price. While explicit geographic or demographic boundaries are not provided, the data reflects sales activity across various outlet types over time, based on the
Outlet_Year attribute.License
The dataset operates under the CC0: Public Domain license.
Who Can Use It
- Students and Beginners: Ideal for introductory projects covering machine learning, particularly regression and feature engineering.
- Retail Strategists: Useful for analysing how store attributes (size, type, age) influence product sales performance.
- Data Scientists: Applicable for testing and refining predictive algorithms aimed at optimising inventory and maximising revenue.
Dataset Name Suggestions
- Mega Mart Sales Prediction Data
- Retail Product Sales Drivers
- Store Profit Forecasting Dataset
- Outlet Sales Performance Metrics
Attributes
Original Data Source: Retail Product Sales Drivers
QUALITY