6 datasets found
  1. Online Sales Dataset - Popular Marketplace Data

    • kaggle.com
    Updated May 25, 2024
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    ShreyanshVerma27 (2024). Online Sales Dataset - Popular Marketplace Data [Dataset]. https://www.kaggle.com/datasets/shreyanshverma27/online-sales-dataset-popular-marketplace-data
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 25, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    ShreyanshVerma27
    License

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

    Description

    This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

    Columns:

    • Order ID: Unique identifier for each sales order.
    • Date:Date of the sales transaction.
    • Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
    • Product Name:Specific name or model of the product sold.
    • Quantity:Number of units of the product sold in the transaction.
    • Unit Price:Price of one unit of the product.
    • Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
    • Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
    • Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

    Insights:

    • 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
    • 2. Explore the popularity of different product categories across regions.
    • 3. Investigate the impact of payment methods on sales volume or revenue.
    • 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
    • 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.
  2. Share of U.S. teens who experienced period poverty as of 2023

    • statista.com
    Updated May 14, 2024
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    Statista (2024). Share of U.S. teens who experienced period poverty as of 2023 [Dataset]. https://www.statista.com/statistics/1242985/us-period-poverty-teenage-students/
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    Dataset updated
    May 14, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 5, 2023 - Sep 10, 2023
    Area covered
    United States
    Description

    A 2023 survey revealed that nearly a quarter of teenagers who menstruate in the U.S. experienced period poverty - the inability to access menstrual hygiene products - because they couldn't afford them. Furthermore, four in ten teens surveyed have worn products longer than recommended. This statistic shows the percentage of teens in the United States who struggled to afford feminine hygiene products as of 2023.

  3. Consumer Expenditure Survey, 2013: Interview Survey and Detailed Expenditure...

    • icpsr.umich.edu
    ascii, delimited +5
    Updated Nov 25, 2015
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    United States Department of Labor. Bureau of Labor Statistics (2015). Consumer Expenditure Survey, 2013: Interview Survey and Detailed Expenditure Files [Dataset]. http://doi.org/10.3886/ICPSR36237.v2
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    excel, stata, spss, delimited, r, ascii, sasAvailable download formats
    Dataset updated
    Nov 25, 2015
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States Department of Labor. Bureau of Labor Statistics
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/36237/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/36237/terms

    Time period covered
    2012 - 2014
    Area covered
    United States
    Description

    The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. The CE program is comprised of two separate components, each with its own questionnaire and independent sample: (1) the quarterly Interview Survey, and (2) the Diary Survey. This data collection contains the quarterly Interview Survey data, which was designed to collect data on major items of expense which respondents could be expected to recall for 3 months or longer. Items include relatively large expenditures, such as those for property, automobiles, and major durable goods, and those that occurred on a regular basis, such as rent or utilities. The Interview Survey does not collect data on expenses for housekeeping supplies, personal care products, and nonprescription drugs, which contribute about 5 to 15 percent of total expenditures. The 2013 Interview Survey contains eight groups of Interview data files (FMLI, MEMI, MTBI, ITBI, ITII, NTAXI, FPAR, and MCHI), forty-three Detailed Expenditure (EXPN) files, and processing files. The FMLI, MEMI, MTBI, ITBI, ITII, and NTAXI files are organized by the calendar quarter of the year in which the data were collected. There are five quarterly datasets for each of these files, running from the first quarter of 2013 through the first quarter of 2014 (with NTAXI files starting the second quarter of 2013). The FMLI file contains consumer unit (CU) characteristics, income, and summary level expenditures; the MEMI file contains member characteristics and income data; the MTBI file contains expenditures organized on a monthly basis at the Universal Classification Code (UCC) level; the ITBI file contains income data converted to a monthly time frame and assigned to UCCs; and the ITII file contains the five imputation variants of the income data converted to a monthly time frame and assigned to UCCs. The NTAXI file contains federal and state tax information for each tax unit within the CU. The FPAR and MCHI datasets are grouped as 2-year datasets (2012 and 2013), plus the first quarter of 2014, and contain paradata about the Interview survey. The FPAR file contains CU level data about the Interview survey, including timing and record use. The MCHI file contains data about each interview contact attempt, including reasons for refusal and times of contact. Both FPAR and MCHI files contain five quarters of data. The EXPN files contain expenditure data and ancillary descriptive information, often not available on the FMLI or MTBI files, in a format similar to the Interview questionnaire. In addition to the extra information available on the EXPN files, users can identify distinct spending categories easily and reduce processing time due to the organization of the files by type of expenditure. Each of the 43 EXPN files contains five quarters of data, directly derived from their respective questionnaire sections. The processing files enhance computer processing and tabulation of data, and provide descriptive information on item codes. There are two types of processing files: (1) aggregation scheme files used in the published consumer expenditure survey interview tables and integrated tables (ISTUB and INTSTUB), and (2) a vehicle make file (CAPIVEHI). The processing files are further explained in the Interview Survey Users' Guide, Section III.H.9. "Processing Files." In addition to the primary users' guide, the Users' Guide to Income Imputation provides information on how to appropriately use the imputed income data. Demographic and family characteristics data include age, sex, race, marital status, and CU relationships for each CU member. Income information was also collected, such as wage, salary, unemployment compensation, child support, and alimony, as well as information on the employment of each CU member age 14 and over. The unpublished integrated CE data tables produced by the BLS are available to download through NADAC (click on "Other" in the Dataset(s) section). The tables show average and percentile expenditures for detailed items, as well as the standard error and coefficient of variation (CV) for each spending

  4. Data from: Consumer Expenditure Survey

    • datacatalog.med.nyu.edu
    Updated Jul 21, 2023
    + more versions
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    United States - Bureau of Labor Statistics (BLS) (2023). Consumer Expenditure Survey [Dataset]. https://datacatalog.med.nyu.edu/dataset/10117
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    Dataset updated
    Jul 21, 2023
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Authors
    United States - Bureau of Labor Statistics (BLS)
    Time period covered
    Jan 1, 1972 - Present
    Area covered
    United States
    Description

    The Consumer Expenditure Survey (CE) consists of two parts: the Quarterly Interview Survey and the Diary Survey. Both surveys provide information on the purchasing habits of American consumers, including data on their expenditures, income, and consumer unit characteristics (e.g., age, education, occupation). The Quarterly Interview Survey (CEQ) includes information on monthly out-of-pocket expenses like housing, apparel, transportation, healthcare, insurance, and entertainment. The Diary Survey (CED) includes information on frequently purchased items like food, beverages, tobacco, personal care products, and nonprescription drugs. Approximately 20,000 independent interview surveys and 11,000 independent diary surveys are completed annually. The United States Bureau of Labor Statistics (BLS) publishes 12-month estimates of consumer expenditures annually, summarized by various income levels and demographic characteristics. Geographic data is available at the national level; for regions, divisions, selected states, and selected metropolitan statistical areas; and by population size of area.

  5. Monthly average retail prices for food and other selected products

    • www150.statcan.gc.ca
    • open.canada.ca
    • +2more
    Updated Mar 16, 2022
    + more versions
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    Government of Canada, Statistics Canada (2022). Monthly average retail prices for food and other selected products [Dataset]. http://doi.org/10.25318/1810000201-eng
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    Dataset updated
    Mar 16, 2022
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Monthly average retail prices for food, household supplies, personal care items, cigarettes and gasoline. Prices are presented for the current month and previous four months. Prices are in Canadian current dollars.

  6. Consumer Expenditure Survey, 2011: Interview Survey and Detailed Expenditure...

    • search.gesis.org
    Updated Apr 19, 2018
    + more versions
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    United States Department of Labor. Bureau of Labor Statistics (2018). Consumer Expenditure Survey, 2011: Interview Survey and Detailed Expenditure Files - Version 1 [Dataset]. http://doi.org/10.3886/ICPSR34441.v1
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    Dataset updated
    Apr 19, 2018
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    GESIS search
    Authors
    United States Department of Labor. Bureau of Labor Statistics
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de450486https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de450486

    Description

    Abstract (en): The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index.The CE program is comprised of two separate components (each with its own questionnaire and independent sample), the quarterly Interview Survey and the Diary Survey (ICPSR 34442). This data collection contains the quarterly Interview Survey data, which was designed to collect data on major items of expense which respondents could be expected to recall for 3 months or longer. These included relatively large expenditures, such as those for property, automobiles, and major durable goods, and those that occurred on a regular basis, such as rent or utilities. The Interview Survey does not collect data on expenses for housekeeping supplies, personal care products, and nonprescription drugs, which contribute about 5 to 15 percent of total expenditures.The microdata in this collection are available as SAS, SPSS, and STATA datasets or ASCII comma-delimited files. The 2011 Interview Survey release contains seven groups of Interview data files (FMLY, MEMB, MTBI, ITBI, ITII, FPAR, and MCHI), 50 EXPN files, and processing files.The FMLY, MEMB, MTBI, ITBI, and ITII files are organized by the calendar quarter of the year in which the data were collected. There are five quarterly datasets for each of these files, running from the first quarter of 2011 through the first quarter of 2012. The FMLY file contains consumer unit (CU) characteristics, income, and summary level expenditures; the MEMB file contains member characteristics and income data; the MTBI file contains expenditures organized on a monthly basis at the Universal Classification Code (UCC) level; the ITBI file contains income data converted to a monthly time frame and assigned to UCCs; and the ITII file contains the five imputation variants of the income data converted to a monthly time frame and assigned to UCCs.The FPAR and MCHI datasets are grouped as 2-year datasets (2010 and 2011), plus the first quarter of the 2012 and contain paradata about the Interview survey. The FPAR file contains CU level data about the Interview survey, including timing and record use. The MCHI file contains data about each interview contact attempt, including reasons for refusal and times of contact. Both FPAR and MCHI files contain five quarters of data.The EXPN files contain expenditure data and ancillary descriptive information, often not available on the FMLY or MTBI files, in a format similar to the Interview questionnaire. In addition to the extra information available on the EXPN files, users can identify distinct spending categories easily and reduce processing time due to the organization of the files by type of expenditure. Each of the 50 EXPN files contains five quarters of data, directly derived from their respective questionnaire sections.The processing files enhance computer processing and tabulation of data, and provide descriptive information on item codes. The processing files are: (1) aggregation scheme files used in the published consumer expenditure survey interview tables and integrated tables (ISTUB and INTSTUB), (2) a UCC file that contains UCCs and their abbreviated titles, identifying the expenditure, income, or demographic item represented by each UCC, (3) a vehicle make file (CAPIVEHI), and (4) files containing sample programs. The processing files are further explained in the Interview User Guide, Section III.G.8. "PROCESSING FILES." There is also a second user guide, User's Guide to Income Imputation in the CE, which includes information on how to appropriately use the imputed income data. Demographic and family characteristics data include age, sex, race, marital status, and CU relationships for each CU member. Income information, such as wage, salary, unemployment compensation, child support, and alimony, as well as information on the employment of each CU member age 14 and over was also collected. The files include weights needed to calculate population estimates and variances. There are 45 weights associated with each consumer unit. Please refer to the User Guide documentation for a detailed explanation of the weight variables used. Eligible population includes all civilian non-i...

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ShreyanshVerma27 (2024). Online Sales Dataset - Popular Marketplace Data [Dataset]. https://www.kaggle.com/datasets/shreyanshverma27/online-sales-dataset-popular-marketplace-data
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Online Sales Dataset - Popular Marketplace Data

Global Transactions Across Various Product Categories

Explore at:
3 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
May 25, 2024
Dataset provided by
Kagglehttp://kaggle.com/
Authors
ShreyanshVerma27
License

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

Description

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

  • Order ID: Unique identifier for each sales order.
  • Date:Date of the sales transaction.
  • Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
  • Product Name:Specific name or model of the product sold.
  • Quantity:Number of units of the product sold in the transaction.
  • Unit Price:Price of one unit of the product.
  • Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
  • Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
  • Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

  • 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
  • 2. Explore the popularity of different product categories across regions.
  • 3. Investigate the impact of payment methods on sales volume or revenue.
  • 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
  • 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.
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