Data
Retail_Transaction_Dataset

Retail_Transaction_Dataset

active ARFF Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) Visibility: public Uploaded 31-05-2024 by Iwo Godzwon
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Description: The Retail_Transaction_Dataset.csv provides a comprehensive overview of various retail transactions, capturing customer behavior, product details, and purchase information. It includes data on customer and product identifiers, quantity and price of items purchased, date and time of transaction, payment methods, store locations, product categories, discounts applied, and the total amount of each transaction. This dataset is formatted in CSV, facilitating ease of use for analysis and reporting purposes. Attribute Description: 1. CustomerID: Unique numeric identifier for customers (e.g., 770728, 151136). 2. ProductID: Alphabetic identifier for products ('A', 'B', 'C'). 3. Quantity: Numeric value representing the number of items purchased (e.g., 7, 5). 4. Price: Numeric value indicating the price of a single item in a transaction, in USD (e.g., 78.11, 72.39). 5. TransactionDate: Date and time of the transaction ('MM/DD/YYYY HH:MM'). 6. PaymentMethod: Mode of payment used for the transaction ('Cash', 'Debit Card', 'PayPal'). 7. StoreLocation: Address of the store where the transaction occurred. 8. ProductCategory: Category of the product purchased ('Home Decor', 'Books', 'Clothing'). 9. DiscountApplied(%): Percentage of discount applied to the transaction. 10. TotalAmount: Total amount paid for the transaction, in USD, after discount. Use Case: The Retail_Transaction_Dataset.csv is an invaluable resource for retail analysis, providing insights into customer purchasing patterns, product popularity, seasonal trends in sales, and effectiveness of discount strategies. It can be utilized by data analysts and marketing professionals to optimize inventory management, develop targeted promotions, and implement dynamic pricing strategies to enhance customer satisfaction and maximize profitability. Additionally, the dataset supports academic research in consumer behavior and retail analytics.

10 features

CustomerIDnumeric95215 unique values
0 missing
ProductIDnominal4 unique values
0 missing
Quantitynumeric9 unique values
0 missing
Pricenumeric100000 unique values
0 missing
TransactionDatestring91025 unique values
0 missing
PaymentMethodnominal4 unique values
0 missing
StoreLocationstring100000 unique values
0 missing
ProductCategorynominal4 unique values
0 missing
DiscountApplied(%)numeric100000 unique values
0 missing
TotalAmountnumeric99998 unique values
0 missing

19 properties

100000
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
5
Number of numeric attributes.
3
Number of nominal attributes.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
50
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
30
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.

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