Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Domestic Food Prices After COVID-19

Analyzing Impact on Developing Countries' Food Security

By [source]

About this dataset

This dataset looks at the effect of the COVID-19 pandemic on food prices in both domestic and international markets, particularly in developing countries. It contains data on monthly changes in food prices, categorised by country, market, price type (domestic or international) and commodities. In particular, this dataset provides insight into how the pandemic has impacted food security for those living in poorer countries where price increases may be more acutely felt. This dataset gives us a greater understanding of these changing dynamics of global food systems to enable more efficient interventions and support for those who are most vulnerable

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How to use the dataset

This dataset is an excellent resource for anyone looking to analyze the impact of COVID-19 on domestic food prices in developing countries. With this dataset, you can get an up-to-date overview of changes in the costs of various commodities in a given market and by a given price type. Additionally, you can filter data by commodity, country and price type.

In order to use this dataset effectively, here are some steps: - Identify your research question(s) - Filter the dataset by selecting specific columns that best answer your research question (ex: month, country, commodity) - Analyze the data accordingly (for example: Sorting the results then calculating averages). - Interpret results into actionable insights or visualizations

Research Ideas

• Analyzing trends in the cost of food items across different countries to understand regional disparities in food insecurity.
• Comparing pre- and post-COVID international food prices to study how nations altered their trade policies in response to the pandemic, indicating a shift towards or away from trading with other nations for food procurement.
• Using sentiment analysis to study consumer sentiment towards purchasing certain items based on their market prices, allowing businesses and governments alike to better target interventions aimed at improving access and availability of food supplies

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: dom_clean_data.csv | Column name | Description | |:---------------|:---------------------------------------------------------------------------| | month | The month in which the data was collected. (Date) | | country | The country in which the data was collected. (String) | | price_type | The type of price (domestic or international) that was collected. (String) | | market | The market in which the data was collected. (String) | | commodity | The type of commodity that was collected. (String) |

File: int_clean_data.csv | Column name | Description | |:---------------|:---------------------------------------------------------------------------| | country | The country in which the data was collected. (String) | | commodity | The type of commodity that was collected. (String) | | price_type | The type of price (domestic or international) that was collected. (String) | | time | The month in which the data was collected. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Cost of food in Canada increased 3.40 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Canada Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kayanza, Ruyigi, Bubanza, Karuzi, Bujumbura Mairie, Muramvya, Gitega, Rumonge, Bururi, Kirundo, Cankuzo, Cibitoke, Muyinga, Rutana, Bujumbura Rural, Makamba, Ngozi, Mwaro, SAHEL, CASCADES, SUD-OUEST, EST, BOUCLE DU MOUHOUN, CENTRE-NORD, PLATEAU-CENTRAL, HAUTS-BASSINS, CENTRE, NORD, CENTRE-SUD, CENTRE-OUEST, CENTRE-EST, Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Nord, Extrême-Nord, Ouest, Nord-Ouest, Adamaoua, Sud-Ouest, Est, Littoral, Centre, Haut-Uele, Nord-Kivu, Ituri, Tshopo, Kwilu, Kasai, Sud-Kivu, Kongo-Central, Nord-Ubangi, Sud-Ubangi, Kasai-Central, Bas-Uele, Tanganyika, Lualaba, Kasai-Oriental, Kwango, Haut-Lomami, Haut-Katanga, Maniema, Kinshasa, Mai-Ndombe, Equateur, Lomami, Likouala, Brazzaville, Point-Noire, Pool, Bouenza, Cuvette, Lekoumou, Nzerekore, Boke, Kindia, Kankan, Faranah, Mamou, Labe, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, North, South, Artibonite, South-East, Grande'Anse, North-East, West, North-West, SULAWESI UTARA, SUMATERA UTARA, KALIMANTAN UTARA, JAWA BARAT, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, SULAWESI SELATAN, JAMBI, JAWA TIMUR, KALIMANTAN SELATAN, BALI, BANTEN, JAWA TENGAH, RIAU, SUMATERA BARAT, KEPULAUAN RIAU, PAPUA, SULAWESI BARAT, BENGKULU, MALUKU UTARA, DAERAH ISTIMEWA YOGYAKARTA, KALIMANTAN BARAT, KALIMANTAN TENGAH, PAPUA BARAT, SUMATERA SELATAN, MALUKU, KEPULAUAN BANGKA BELITUNG, ACEH, DKI JAKARTA, SULAWESI TENGGARA, KALIMANTAN TIMUR, LAMPUNG, GORONTALO, SULAWESI TENGAH, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, North Eastern, Rift Valley, Coast, Eastern, Nairobi, , Central, Nyanza, Attapeu, Louangnamtha, Champasack, Bokeo, Bolikhamxai, Khammouan, Oudomxai, Phongsaly, Vientiane, Xiengkhouang, Louangphabang, Salavan, Savannakhet, Sekong, Vientiane Capital, Houaphan, Xaignabouly, Akkar, Mount Lebanon, Baalbek-El Hermel, Beirut, Bekaa, El Nabatieh, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, East, North Central, Uva, Western, Sabaragamuwa, Southern, Northern, North Western, Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Yangon, Rakhine, Shan (North), Kayin, Kachin, Shan (South), Mon, Tanintharyi, Mandalay, Sagaing, Kayah, Shan (East), Chin, Magway, Bago (East), Zambezia, Cabo_Delgado, Tete, Manica, Sofala, Maputo, Gaza, Niassa, Inhambane, Maputo City, Nampula, Hodh Ech Chargi, Hodh El Gharbi, Brakna, Adrar, Assaba, Guidimakha, Gorgol, Trarza, Tagant, Dakhlet-Nouadhibou, Nouakchott, Tiris-Zemmour, Central Region, Southern Region, Northern Region, Tillaberi, Tahoua, Agadez, Zinder, Dosso, Niamey, Maradi, Diffa, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Adamawa, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Cordillera Administrative region, Region XIII, Region VI, Region V, Region III, Autonomous region in Muslim Mindanao, Region IV-A, Region VIII, Region VII, Region X, Region II, Region IV-B, Region XII, Region XI, Region I, National Capital region, Region IX, North Darfur, Blue Nile, Nile, Eastern Darfur, West Kordofan, Gedaref, West Darfur, North Kordofan, South Kordofan, Kassala, Khartoum, White Nile, South Darfur, Red Sea, Sennar, Al Gezira, Central Darfur, Tambacounda, Diourbel, Ziguinchor, Kaffrine, Dakar, Saint Louis, Fatick, Kolda, Louga, Kaolack, Kedougou, Matam, Thies, Sedhiou, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Eastern Equatoria, Central Equatoria, Western Bahr el Ghazal, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

Food Prices Year By Year

Findings and Implications

About this dataset

In 2022, the world may face a global food crisis. This dataset includes information on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This data is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis. With this data, we can better understand the factors that may contribute to the crisis and work towards finding solutions that could help prevent or mitigate its effects

How to use the dataset

This dataset contains information on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This data is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis.

To use this dataset effectively, researchers should focus on the trends in food prices over time. Additionally, they should look at the relationships between different types of food prices. For example, does an increase in meat price lead to a corresponding increase in dairy price? Finally, researchers should also consider how other factors such as oil price or sugar price may impact food prices

Research Ideas

1. Identifying choke points in the global food supply chain
2. Estimating the economic impact of a potential global food crisis
3. Developing policies to mitigate the impact of a potential global food crisis

Acknowledgements

We would like to thank the Department of Agriculture for their data on food prices, meat prices, dairy prices, cereal prices, oil prices, and sugar prices. This dataset is of utmost importance to researchers as it will help inform their work on finding solutions to this potential crisis

License

See the dataset description for more information.

Columns

File: FAOFP1990_2022.csv

Food Price Index in World decreased to 126.40 Index Points in October from 128.50 Index Points in September of 2025. This dataset includes a chart with historical data for World Food Price Index.

Description

This dataset contains Countries, Commodities, and Markets data, sourced from the World Food Programme Price Database. The volume of data means that the actual Food Prices data is in country-level datasets. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

Acknowledgements & Source

Compiled by the World Food Program and distributed by HDX.

Image Source

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Australia Grocery Product Dataset

Products, prices, titles, descriptions, etc..

By Jeff [source]

About this dataset

This dataset provides information on grocery products available in Australia, including pricing information. The data was extracted from the Grocery department of coles.com.au, and includes a selected list of categories. Columns include postal code, category, subcategory, product group, product name, package price, price per unit, package size, estimated status, special status, stock status, retail price, product URL, brand, SKU number, run date, unit price, and unit price unit

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Research Ideas

• Create a retail price comparison tool for grocery items in different Australian states
• Use the data to analyze trends in grocery pricing over time
• Use the data to map out the cheapest and most expensive areas for groceries in Australia

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

See the dataset description for more information.

Columns

File: Australia_Grocery_2022Sep.csv | Column name | Description | |:--------------------|:-------------------------------------------------------------------| | Postal_code | The postal code of the store where the product was found. (String) | | Category | The category of the product. (String) | | Sub_category | The subcategory of the product. (String) | | Product_Group | The product group of the product. (String) | | Product_Name | The name of the product. (String) | | Package_price | The price of the product in Australian dollars. (Float) | | Price_per_unit | The price per unit of the product in Australian dollars. (Float) | | package_size | The size of the product package in grams. (Integer) | | is_estimated | Whether or not the price is an estimate. (Boolean) | | is_special | Whether or not the product is on special. (Boolean) | | in_stock | Whether or not the product is in stock. (Boolean) | | Retail_price | The retail price of the product in Australian dollars. (Float) | | Product_Url | The URL of the product on the Coles website. (String) | | Brand | The brand of the product. (String) | | Sku | The SKU of the product. (String) | | RunDate | The date on which the price was collected. (Date) | | unit_price | The unit price of the product in Australian dollars. (Float) | | unit_price_unit | The unit of measurement for the unit price. (String) | | state | The state in which the product was found. (String) | | city | The city in which the product was found. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jeff.

Cost of food in Lebanon increased 23.90 percent in September of 2025 over the same month in the previous year. This dataset provides - Lebanon Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more. How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources. How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Tillaberi, Tahoua, Agadez, Zinder, Dosso, Niamey, Maradi, Diffa, Market Average

Context

This is a relatively small dataset of the USDA Monthly Cost of Food report. The data given is from January of each year from 2000-2021. The report details expected food costs broken down by demographic and year, as well as food expenditure level, from a "Thrifty" food plan which describes careful budgeted food purchasing, to a "Liberal" food plan which entails more open food purchasing.

Content

This data was sourced directly from the USDA Food Plans website and read/converted from PDF form using Tabula

Acknowledgements

USDA Food Plans: https://www.fns.usda.gov/cnpp/usda-food-plans-cost-food-reports-monthly-reports Photo by Jakub Kapusnak on Unsplash

Inspiration

How have food costs increased in the last 20 years? Have food costs kept pace with overall inflation, or are they higher or lower than expected? What should projected food costs be going forward?

Like this dataset? See my other datasets!

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: North, South, Artibonite, Centre, South-East, Grande'Anse, North-East, West, North-West, Market Average

Monthly average retail prices for selected products, for Canada, provinces, Whitehorse and Yellowknife. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

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.

Context

This dataset consists of 300k+ records of tortilla prices from Mexico's national System of Information and Market Integration, which surveys 53 cities, 384 mom-and-pop stores, and 120 retail stores that sell "tortillas" throughout Mexico.

Mexico's Bureau of Economic Affairs publishes the information on this site based on a survey made across the whole country. Still, it is not very user-friendly, so the information since 2007 was downloaded and stored in a single, easy-to-use CSV file.

The price on each record consists of the mean prices for all observations made on that day, in that city, and in that state. The price shown in the file is for 1 (one) kilogram of tortillas in Mexican pesos ($MXN).

If you don't know what a "tortilla" is, the article in Wikipedia is a good start to get you up and running.

Inspiration

Tortilla is one of Mexico's most important foods. It is made almost entirely of milled corn and water, which forms a dough that is cooked for some minutes before being stored and ready to sell. It is similar to Naan bread, commonly known for its use in Indian cuisine, but made out of corn instead of wheat. Tortillas are sold in packages of 1 kilogram, which, depending on their size, can have around 40 to 50 tortillas per kilogram. Mom-and-pop stores can sell tortillas in fractions of kilograms.

This dataset contains information from both mom-and-pop stores (small stores located near residential areas dedicated solely to selling fresh tortillas) and from big retailers (such as Walmart, which sells tortillas in Mexico in almost all of its stores).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2718659%2Ff918235c86a0d2183807b041a024f118%2Fmom-and-pop-store.png?generation=1709524219666519&alt=media" alt="mom-and-pop-store">

Example of a typical mom-and-pop store (aka "Tortillería") in Mexico

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2718659%2F6b16090673e26a509aaebc4e89b207b2%2FWalmart-Tortilleria.jpg?generation=1709524326553327&alt=media" alt="">

Example of a stand selling tortillas in Walmart

Several interesting facts can be made regarding the price of tortillas in these two types of stores... surprisingly, retail stores sell tortillas way below the prices of mom-and-pop stores, while at the same time, mom-and-pop stores usually sell tortillas to people with less income than those who buy them in a retail store.

The price difference between retailers and mom-and-pop stores has increased since the COVID-19 pandemic, as illustrated in the following figure.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2718659%2F88cba385cef8b043ef662859e66a7b71%2Flineplot_by_type_2007-2024.png?generation=1709523281176095&alt=media" alt="">

The purpose of publishing this dataset is to raise awareness of the importance of food price monitoring and the impact those prices can have on people's lives.

Thumbnail photo by Louis Hansel on Unsplash

Cost of food in European Union increased 3 percent in October of 2025 over the same month in the previous year. This dataset provides - European Union Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table contains data on the average cost of a market basket of nutritious food items relative to income for female-headed households with children, for California, its regions, counties, and cities/towns. The ratio uses data from the U.S. Department of Agriculture and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. An adequate, nutritious diet is a necessity at all stages of life. Inadequate diets can impair intellectual performance and have been linked to more frequent school absence and poorer educational achievement in children. Nutrition also plays a significant role in causing or preventing a number of illnesses, such as cardiovascular disease, some cancers, obesity, type 2 diabetes, and anemia. At least two factors influence the affordability of food and the dietary choices of families – the cost of food and family income. The inability to afford food is a major factor in food insecurity, which has a spectrum of effects including anxiety over food sufficiency or food shortages; reduced quality or desirability of diet; and disrupted eating patterns and reduced food intake. More information about the data table and a data dictionary can be found in the Attachments.

Introduction Unlock powerful insights with the Target Groceries Data dataset, featuring an extensive collection of over thousands of grocery products sold at Target stores across the USA. This dataset, available in CSV format, is ideal for analysts, researchers, and marketers who want to explore product details, pricing trends, and consumer preferences in one of the biggest retail chains in the United States.

With detailed information on groceries from various categories, including fresh produce, packaged foods, snacks, beverages, and more, this dataset empowers you to perform in-depth market analysis, track product performance, and understand consumer shopping behaviors.

Key Features of the Target Groceries Data Dataset

The Target Groceries Data dataset provides a wide range of attributes that allow you to analyze grocery products across multiple dimensions. Key data points include:

• Title: Name of the grocery product.
• URL: Direct link to the product page on the Target website.
• Brand: The brand associated with the product.
• Main Image: Primary image of the product for easy visual identification.
• SKU: Unique product identifier for inventory management.
• Description: A detailed description of the product, including key features and uses.
• Raw Description: The unprocessed, full product description as listed on the Target website.
• GTIN13: Global Trade Item Number (GTIN13), a unique identifier for the product.
• Currency: Currency used for the product price (usually USD for the USA).
• Price: The listed price of the product.
• Availability: Whether the product is in stock or out of stock.
• Available Delivery Method: Options available for delivery (e.g., standard shipping, same-day delivery).
• Available Branch: Locations where the product is available for pickup.
• Primary Category: The main category the product falls under (e.g., Grocery, Household).
• Sub Category 1, 2, 3: Additional category breakdowns for better classification (e.g., Snacks, Beverages, Organic).
• Images: Additional product images.
• Raw Specifications: The full list of product specifications in raw text format.
• Specifications: Structured version of the product specifications for easy analysis.
• Highlights: Key selling points or product features.
• Raw Highlights: Raw text from product highlights.
• Uniq ID: Unique identifier for each product record in the dataset.
• Scraped At: Timestamp for when the data was scraped, ensuring data freshness.

Why Use the Target Groceries Data?

1. Comprehensive Market Research: Dive deep into the grocery market by analyzing product prices, availability, and consumer favorites across multiple categories.
2. Pricing Strategy Insights: Leverage data on prices, discounts, and availability to assess competitive pricing strategies and optimize your own.
3. Consumer Behavior Analysis: With product descriptions, highlights, and specifications, understand what influences consumer purchasing decisions.
4. Product Performance Tracking: Analyze how products in various categories are performing, identify popular brands, and track trends over time using scraped_at timestamps.
5. Optimized Inventory Management: Utilize SKU, availability, and delivery method data for efficient inventory tracking and management.

How to Access the Dataset The Target Groceries Data is available in CSV format, making it easy to import into any data analysis tool for in-depth analysis. Whether you're a business strategist, data scientist, or researcher, this dataset offers everything you need to gain valuable insights into the grocery retail market.

Conclusion Enhance your analysis of the grocery sector with the Target Groceries Data. With detailed attributes such as product titles, pricing, availability, and specifications, this dataset provides everything you need to make informed, data-driven decisions in the competitive grocery market.

Dataset Structure & Description

https://imgur.com/AYzsmYU.jpg" alt="Dataset Structure">

Context and Inspiration

I read an article yesterday which got my mind storming, A article by Worldbank on August 15th, 2022 better explains it, It has been quoted below,
I already have a project i'm working on since Feb 2021, trying to solving this problem, listed in my datasets

This dataset showcases the statistics over the past 6-7 decades which covers the production of 150+ unique crops, 50+ livestock elements, Land distribution by usage and population, As aspiring data scientists one can try to extract insights incentivizing the optimal use of natural resources and distribution of resources

August 15, 2022 - Worldbank

Record high food prices have triggered a global crisis that will drive millions more into extreme poverty, magnifying hunger and malnutrition, while threatening to erase hard-won gains in development. The war in Ukraine, supply chain disruptions, and the continued economic fallout of the COVID-19 pandemic are reversing years of development gains and pushing food prices to all-time highs. Rising food prices have a greater impact on people in low- and middle-income countries, since they spend a larger share of their income on food than people in high-income countries. This brief looks at rising food insecurity and World Bank responses to date.

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Hello 👋, If you are enjoying so far, Please checkout my other Datasets, I would love to hear your support & feedback on it, Thank you !

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