This data is artificially generated. It can be used for practicing data visualization and analysis skills. Please note that since the data is generated randomly, it may not reflect real-world sales data accurately. However, it should serve as a good starting point for practicing data analysis and visualization.

Description :

• Sales Date: This column contains the date of each sale. The dates are generated for a period of 120 days starting from January 1, 2023. • Category: This column contains the category of the product sold. The categories include ‘Electronics’, ‘Clothing’, and ‘Home & Kitchen’. • Subcategory: This column contains the subcategory of the product sold. Each category has its own set of subcategories. For example, the ‘Electronics’ category includes subcategories such as ‘Communication’, ‘Computers’, and ‘Wearables’. • ProductName: This column contains the name of the product sold. Each subcategory has its own set of products. For example, the ‘Communication’ subcategory includes products such as ‘Walkie Talkie’, ‘Cell Phone’, and ‘Smart Phone’. • Salesperson: This column contains the name of the salesperson who made the sale. There are different salespersons assigned to each category. • Gender: This column contains the gender of the salesperson. The gender is determined based on the salesperson’s name. • Unit sold: This column contains the number of units of the product sold in the sale. The number of units sold is a random number between 1 and 100. • Original Price: This column contains the original price of the product. The original price is a random number between 10 and 1000. • Sales Price: This column contains the sales price of the product. The sales price is calculated as a random fraction of the original price, ensuring that the sales price is always slightly higher than the original price.

For information on 'How to generate a dataset', click here.

This is a synthetic dataset generated using AI to simulate clothing retail sales transactions across various Indian cities. It contains 10,000 records covering sales, customers, products, and salesperson details, along with pricing, profit margins, and transaction status.

Real-world retail sales data is often private and commercially restricted, especially for fashion and apparel brands. To overcome this limitation, this dataset was generated using AI-driven synthetic data generation techniques to simulate realistic sales behavior, product diversity, and regional trends.

This synthetic data mimics how an eCommerce clothing store might record daily sales across different locations in India, enabling data science enthusiasts to explore and build models safely without privacy issues.

Purpose: Designed for educational and analytical use — ideal for:

Sales forecasting and time-series prediction

Profit margin analysis

Customer segmentation

Retail business dashboard creation

Dataset Structure:

🏷️ saleID, saleDate: Unique transaction details

👗 productID, productName, productCategory, productColor, productSize: Product information

💰 unitPrice, costPrice, totalAmount, totalCost: Pricing and profitability fields

👤 customerID, customerName, location: Customer and location data

🧑‍💼 salespersonID, salespersonName, status, salesChannel: Sales and fulfillment info

Data Origin: The dataset was AI-generated using rule-based data simulation to resemble realistic retail sales trends while preserving full anonymity. No real customer or company data is included.

Potential Use Cases:

• Predicting sales performance

• Analyzing pricing strategies

• Training regression or classification models on business outcomes

• Building Power BI or Tableau dashboards

Global Super Store Sales Data :

Columns Present: 1.) Salesperson
2.) Product 3.) Region
4.) Customer
5.) Date
6.) Item Cost
7.) No.Items
8.) Revenue 9.) Margin