The Retail Sales Transactions Dataset is a synthetic dataset containing 200 rows of sales-related records designed for learning and practice in analytics. It combines both numerical and categorical variables, making it versatile for exploring multiple analytical techniques. Numerical columns include quantity, price per unit, total sales, and discount percent, which provide insights into financial performance. Categorical columns such as product category, store location, payment method, and customer type add dimensions for segmentation and comparison. This dataset is ideal for practicing data cleaning, visualization, SQL queries, and machine learning models, offering realistic business-style data in a simple, structured format.
Rows: 200
Columns: 9
- id (Unique Identifier)
- Numerical:
quantity,price_per_unit,total_sales,discount_percent - Categorical:
product_category,store_location,payment_method,customer_type
Download the dataset from: https://github.com/slidescope/Retail-Sales-Transactions-Data-Analysis-Dashboard-Power-BI
🎯 Purpose of the Dataset
This dataset is designed to simulate retail transactions, making it perfect for:
- Practicing sales data analysis
- Exploring customer behavior
- Creating dashboards in Tableau/Power BI
- Running SQL queries on retail sales
- Building predictive models (e.g., predicting sales or customer type)
❓ Example Questions You Can Solve
- Which store location generates the highest sales?
- Do VIP customers spend more than regular ones?
- What is the average discount given per product category?
- Which payment method is most popular?
- Is there a correlation between quantity and discount?
- Which product category has the highest total revenue?
Beyond simple analysis, this dataset can also be applied to advanced techniques like predictive modeling and customer segmentation. For example, by analyzing purchase patterns, businesses can identify which customer types are most likely to respond to promotions or loyalty programs. Analysts can also compare sales performance across regions to understand market demand and optimize store operations. With both numerical and categorical fields, this dataset is a great resource for practicing data cleaning, visualization, and machine learning algorithms. Whether you’re a beginner or an experienced analyst, it provides realistic retail data for hands-on learning and project building.