6 datasets found
  1. Vital Food Costs: A Five-Nation Analysis 2018-2022

    • kaggle.com
    Updated Jul 16, 2023
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    Suman Goda (2023). Vital Food Costs: A Five-Nation Analysis 2018-2022 [Dataset]. https://www.kaggle.com/sumangoda/food-prices/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 16, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Suman Goda
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    This dataset provides an analysis of average monthly prices for four essential food items, namely Eggs, Milk, Bread, and Potatoes, in five different countries: Australia, Japan, Canada, South Africa, and Sweden. The dataset spans a five-year period, from 2018 to 2022, offering a comprehensive overview of how food prices have evolved over time in these nations.

    The dataset includes information on the average monthly prices of each food item in the respective countries. This information can be valuable for studying and comparing the cost of living, assessing economic trends, and understanding variations in food price dynamics across different regions.

    Use Cases:

    Comparative Analysis: Researchers and analysts can compare food prices across the five countries over the five-year period to identify patterns, trends, and variations. This analysis can help understand differences in purchasing power and economic factors impacting food costs.

    Cost of Living Studies: The dataset can be used to examine the cost of living in different countries, specifically focusing on the expenses related to basic food items. This information can be beneficial for individuals considering relocation or policymakers aiming to evaluate living standards.

    Economic Studies: Economists and policymakers can utilize this dataset to analyze the impact of economic factors, such as inflation or currency fluctuations, on food prices in different countries. It can provide insights into the stability and volatility of food markets in each region.

    Forecasting and Planning: Businesses in the food industry can leverage the dataset to forecast future food price trends and plan their operations accordingly. The historical data can serve as a foundation for predictive models and assist in optimizing pricing strategies and supply chain management.

    Note: The dataset is based on average monthly prices and does not capture individual variations or specific regions within each country. Further analysis and interpretation should consider additional factors like seasonal influences, local market dynamics, and consumer preferences.

  2. T

    Steel - Price Data

    • tradingeconomics.com
    • ru.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Oct 22, 2016
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    TRADING ECONOMICS (2016). Steel - Price Data [Dataset]. https://tradingeconomics.com/commodity/steel
    Explore at:
    xml, csv, excel, jsonAvailable download formats
    Dataset updated
    Oct 22, 2016
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Mar 27, 2009 - Jul 24, 2025
    Area covered
    World
    Description

    Steel rose to 3,228 CNY/T on July 24, 2025, up 0.62% from the previous day. Over the past month, Steel's price has risen 9.54%, and is up 2.93% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Steel - values, historical data, forecasts and news - updated on July of 2025.

  3. d

    Pixta AI | Imagery Data | Global | 10,000 Stock Images | Annotation and...

    • datarade.ai
    .json, .xml, .csv
    Updated Nov 12, 2022
    + more versions
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    Pixta AI (2022). Pixta AI | Imagery Data | Global | 10,000 Stock Images | Annotation and Labelling Services Provided | Traffic scenes from high view for AI & ML [Dataset]. https://datarade.ai/data-products/10-000-traffic-scenes-from-high-view-for-ai-ml-model-pixta-ai
    Explore at:
    .json, .xml, .csvAvailable download formats
    Dataset updated
    Nov 12, 2022
    Dataset authored and provided by
    Pixta AI
    Area covered
    Korea (Republic of), Singapore, New Zealand, Malaysia, Japan, Taiwan, Canada, Hong Kong, Australia, United States of America
    Description
    1. Overview This dataset is a collection of high view traffic images in multiple scenes, backgrounds and lighting conditions that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

    2. Use case This dataset is used for AI solutions training & testing in various cases: Traffic monitoring, Traffic camera system, Vehicle flow estimation,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

    3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ for more details.

  4. T

    Coal - Price Data

    • tradingeconomics.com
    • tr.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Oct 23, 2016
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    TRADING ECONOMICS (2016). Coal - Price Data [Dataset]. https://tradingeconomics.com/commodity/coal
    Explore at:
    csv, xml, json, excelAvailable download formats
    Dataset updated
    Oct 23, 2016
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Dec 5, 2008 - Jul 22, 2025
    Area covered
    World
    Description

    Coal rose to 110.10 USD/T on July 22, 2025, up 0.23% from the previous day. Over the past month, Coal's price has risen 2.66%, but it is still 18.26% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coal - values, historical data, forecasts and news - updated on July of 2025.

  5. T

    HRC Steel - Price Data

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Share
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    TRADING ECONOMICS, HRC Steel - Price Data [Dataset]. https://tradingeconomics.com/commodity/hrc-steel
    Explore at:
    csv, json, xml, excelAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Oct 20, 2008 - Jul 24, 2025
    Area covered
    World
    Description

    HRC Steel fell to 872.95 USD/T on July 24, 2025, down 0.12% from the previous day. Over the past month, HRC Steel's price has fallen 1.81%, but it is still 31.47% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for HRC Steel.

  6. T

    Propane - Price Data

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Oct 22, 2016
    Share
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    TRADING ECONOMICS (2016). Propane - Price Data [Dataset]. https://tradingeconomics.com/commodity/propane
    Explore at:
    xml, excel, json, csvAvailable download formats
    Dataset updated
    Oct 22, 2016
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Oct 27, 2009 - Jul 23, 2025
    Area covered
    World
    Description

    Propane rose to 0.72 USD/Gal on July 23, 2025, up 0.84% from the previous day. Over the past month, Propane's price has fallen 6.94%, and is down 9.56% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Propane - values, historical data, forecasts and news - updated on July of 2025.

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Suman Goda (2023). Vital Food Costs: A Five-Nation Analysis 2018-2022 [Dataset]. https://www.kaggle.com/sumangoda/food-prices/discussion
Organization logo

Vital Food Costs: A Five-Nation Analysis 2018-2022

Comparing Vital Food Costs in Australia, Japan, Canada, South Africa & Sweden

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jul 16, 2023
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Suman Goda
License

Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically

Description

This dataset provides an analysis of average monthly prices for four essential food items, namely Eggs, Milk, Bread, and Potatoes, in five different countries: Australia, Japan, Canada, South Africa, and Sweden. The dataset spans a five-year period, from 2018 to 2022, offering a comprehensive overview of how food prices have evolved over time in these nations.

The dataset includes information on the average monthly prices of each food item in the respective countries. This information can be valuable for studying and comparing the cost of living, assessing economic trends, and understanding variations in food price dynamics across different regions.

Use Cases:

Comparative Analysis: Researchers and analysts can compare food prices across the five countries over the five-year period to identify patterns, trends, and variations. This analysis can help understand differences in purchasing power and economic factors impacting food costs.

Cost of Living Studies: The dataset can be used to examine the cost of living in different countries, specifically focusing on the expenses related to basic food items. This information can be beneficial for individuals considering relocation or policymakers aiming to evaluate living standards.

Economic Studies: Economists and policymakers can utilize this dataset to analyze the impact of economic factors, such as inflation or currency fluctuations, on food prices in different countries. It can provide insights into the stability and volatility of food markets in each region.

Forecasting and Planning: Businesses in the food industry can leverage the dataset to forecast future food price trends and plan their operations accordingly. The historical data can serve as a foundation for predictive models and assist in optimizing pricing strategies and supply chain management.

Note: The dataset is based on average monthly prices and does not capture individual variations or specific regions within each country. Further analysis and interpretation should consider additional factors like seasonal influences, local market dynamics, and consumer preferences.

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