/onlineretail2

Online Retail II Dataset

Primary LanguageR

onlineretail2

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Transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011. This dataset is included in this package with permission of the donor, Dr. Daqing Chen, and may be used only for non-commercial purposes.

The dataset consists of a data frame with eight variables:

  • Invoice: A character indicating the invoice number, which is a 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation.
  • StockCode: A character indicating the product (item) code, which is a 5-digit integral number uniquely assigned to each distinct product. It can be accompanied by a trailing uppercase letter.
  • Description: A character indicating the Product (item) name.
  • Quantity: A numeric indicating the quantities of each product (item) per transaction.
  • InvoiceDate: A POSIXct indicating the invoice day and time when a transaction was generated.
  • Price: A numeric indicating the product price per unit in sterling (£).
  • CustomerID: A numeric indicating the customer number, which is a 5-digit integral number uniquely assigned to each customer.
  • Country: A character indicating the name of the country where a customer resides.

References

Chen, D., Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [https://www.ncbi.nlm.nih.gov/pubmed/23979570]

Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [http://www.inderscience.com/offer.php?id=75325]

Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019.

Ale, L., Zhang, N., Wu, H., Chen, D. and Han T. (2019), Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530.

Singh, R., Graves, J. A., Talbert, D. A., Eberle, W. (2018), Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324.

Source

UCI Machine Learning Repository