Online Retail

Donated on 11/5/2015

This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.

Dataset Characteristics

Multivariate, Sequential, Time-Series

Subject Area

Business

Associated Tasks

Classification, Clustering

Feature Type

Integer, Real

# Instances

541909

# Features

6

Dataset Information

Additional Information

This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Has Missing Values?

No

Introductory Paper

Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining

By Daqing Chen, Sai Laing Sain, Kun Guo. 2012

Published in Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
InvoiceNoIDCategoricala 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellationno
StockCodeIDCategoricala 5-digit integral number uniquely assigned to each distinct productno
DescriptionFeatureCategoricalproduct nameno
QuantityFeatureIntegerthe quantities of each product (item) per transactionno
InvoiceDateFeatureDatethe day and time when each transaction was generatedno
UnitPriceFeatureContinuousproduct price per unitsterlingno
CustomerIDFeatureCategoricala 5-digit integral number uniquely assigned to each customerno
CountryFeatureCategoricalthe name of the country where each customer residesno

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Additional Variable Information

InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invoice Date and time. Numeric, the day and time when each transaction was generated. UnitPrice: Unit price. Numeric, Product price per unit in sterling. CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal, the name of the country where each customer resides.

Papers Citing this Dataset

Finding Robust Itemsets Under Subsampling

By Nikolaj Tatti, Fabian Moerchen, Toon Calders. 2019

Published in ArXiv.

Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries

By Edward Gan, Jialin Ding, Kai Tai, Vatsal Sharan, Peter Bailis. 2018

Published in ArXiv.

Efficient and Scalable Multi-task Regression on Massive Number of Tasks

By Xiao He, Francesco Alesiani, Ammar Shaker. 2018

Published in ArXiv.

Efficient Mining Top-k Regular-Frequent Itemset Using Compressed Tidsets

By Komate Amphawan, Philippe Lenca, Athasit Surarerks. 2011

Published in PAKDD Workshops.

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8 citations
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Keywords

sales

Creators

Daqing Chen

chend@lsbu.ac.uk

School of Engineering, London South Bank University

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