Supermarket Sales Dataset

This dataset provides a historical record of three months of sales data from three different supermarket branches, capturing competitive market trends in populous cities. It’s structured to facilitate predictive analytics in retail.

Dataset Attributes:

• Invoice ID: Unique identifier for each sales transaction.
• Branch: Identifier for each of the three supermarket branches (A, B, and C).
• City: Supermarket locations.
• Customer Type: Distinguishes between ‘Member’ and ‘Normal’ customers.
• Gender: Customer gender.
• Product Line: Categories include Electronic Accessories, Fashion Accessories, Food and Beverages, Health and Beauty, Home and Lifestyle, Sports and Travel.
• Unit Price: Cost per item in dollars.
• Quantity: Items purchased per transaction.
• Tax: Applied sales tax (5%).
• Total: Total transaction cost, including tax.
• Date: Date of transaction, from January 2019 to March 2019.
• Time: Time of transaction, from 10 AM to 9 PM.
• Payment Method: Includes Cash, Credit Card, and Ewallet.
• COGS: Cost of goods sold.
• Gross Margin Percentage: Percentage of gross margin.
• Gross Income: Revenue generated from sales.

This structured data supports analysis of buying patterns, customer behavior, and financial performance across different locations and times.