4 datasets found
  1. Data from: Online retail dataset

    • kaggle.com
    zip
    Updated Nov 20, 2019
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    Lakshmipathi N (2019). Online retail dataset [Dataset]. https://www.kaggle.com/lakshmi25npathi/online-retail-dataset
    Explore at:
    zip(45407886 bytes)Available download formats
    Dataset updated
    Nov 20, 2019
    Authors
    Lakshmipathi N
    Description

    Abstract: A real online retail transaction data set of two years.

    Data Set Information: This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

    Attribute Information: InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the 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: Invice date and time. Numeric. The day and time when a 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 a customer resides.

    Source: Dr. Daqing Chen, Course Director: MSc Data Science. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

    Please find more information refer the below link, https://archive.ics.uci.edu/ml/datasets/Online+Retail+II

  2. Online Retail Transaction Data

    • kaggle.com
    Updated Dec 21, 2023
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    The Devastator (2023). Online Retail Transaction Data [Dataset]. https://www.kaggle.com/datasets/thedevastator/online-retail-transaction-data
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 21, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    The Devastator
    Description

    Online Retail Transaction Data

    UK Online Retail Sales and Customer Transaction Data

    By UCI [source]

    About this dataset

    Comprehensive Dataset on Online Retail Sales and Customer Data

    Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

    This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

    The available attributes within this dataset offer valuable pieces of information:

    • InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

    • StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

    • Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

    • Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

    • InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

    • UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

    Finally,

    • Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

    This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

    Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

    How to use the dataset

    1. Sales Analysis:

    Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

    2. Product Analysis:

    Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

    3. Customer Segmentation:

    If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

    4. Geographical Analysis:

    The Country column enables analysts to study purchase patterns across different geographical locations.

    Practical applications

    Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

  3. UCI Machine Learning Online Retail Transactions

    • kaggle.com
    Updated Sep 2, 2024
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    Denis Expósito (2024). UCI Machine Learning Online Retail Transactions [Dataset]. http://doi.org/10.34740/kaggle/dsv/9303150
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 2, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Denis Expósito
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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. Data was obtained from the UCI Machine Learning public repository

  4. Data from: Online Retail Data Set

    • kaggle.com
    Updated Sep 11, 2021
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    Sahar Pourahmad (2021). Online Retail Data Set [Dataset]. https://www.kaggle.com/datasets/saharpourahmad/online-retail-data-set/suggestions
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 11, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sahar Pourahmad
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Dataset

    This dataset was created by Sahar Pourahmad

    Released under CC0: Public Domain

    Contents

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Lakshmipathi N (2019). Online retail dataset [Dataset]. https://www.kaggle.com/lakshmi25npathi/online-retail-dataset
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Data from: Online retail dataset

A real online retail transaction data set of two years

Related Article
Explore at:
zip(45407886 bytes)Available download formats
Dataset updated
Nov 20, 2019
Authors
Lakshmipathi N
Description

Abstract: A real online retail transaction data set of two years.

Data Set Information: This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Attribute Information: InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the 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: Invice date and time. Numeric. The day and time when a 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 a customer resides.

Source: Dr. Daqing Chen, Course Director: MSc Data Science. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Please find more information refer the below link, https://archive.ics.uci.edu/ml/datasets/Online+Retail+II

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