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Retail Basket Analysis Data
Food & Beverage Consumption
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About
This dataset contains 30,000 unique retail transactions, each representing a customer's shopping basket in a simulated grocery store environment. The data was generated with realistic product combinations and purchase patterns, making it ideal for association rule mining, recommendation systems, and market basket analysis. The dataset includes products across various categories such as beverages, snacks, dairy, household items, fruits, vegetables, and frozen foods. This data is entirely synthetic and does not contain any real user information.
Columns
- TransactionID: A unique identifier for each transaction.
- CustomerID: An anonymous customer identifier.
- Products: A comma-separated list of all products purchased in the transaction.
- Timestamp: The date on which the transaction took place.
Distribution
The dataset comprises 30,000 unique retail transactions. It is typically provided as a data file, often in a CSV format. The total number of values for the TransactionID column is confirmed as 30,000.
Usage
This dataset is perfectly suited for a variety of analytical applications, including:
- Association Rule Mining: Discovering relationships and patterns between products frequently purchased together.
- Recommendation Systems: Developing and testing algorithms designed to suggest products to customers based on their purchase history or similar behaviours.
- Market Basket Analysis: Understanding customer purchasing behaviour, identifying product co-occurrence, and optimising product placements or promotions.
Coverage
The dataset's scope is global, simulating grocery retail transactions without specific geographic limitations. The time range covered by the transaction data is from 1st January 2025 to 30th April 2025. As the data is synthetic, it does not include any real demographic information, ensuring privacy and broad applicability for simulation and modelling.
License
CCO
Who Can Use It
This dataset is particularly valuable for:
- Data Scientists and Analysts: For developing and testing machine learning models related to retail analytics and consumer behaviour.
- Researchers: Studying purchasing patterns, market trends, and the effectiveness of different analytical approaches in a simulated retail environment.
- Students: Learning and practising fundamental data analysis techniques such as association rule mining and building recommendation engines.
- Businesses: Gaining insights into hypothetical customer purchasing patterns, which can inform strategic planning, inventory management, and marketing initiatives.
Dataset Name Suggestions
- Simulated Grocery Retail Transactions
- Retail Basket Analysis Data
- Synthetic Customer Purchase Log
- Grocery Store Transaction Simulation
- Market Basket Dataset for Analytics
Attributes
Original Data Source: Retail Transaction Dataset
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