The "Blinkit Grocery Dataset" appears to be a fictional dataset designed for a grocery or retail scenario, possibly for analytical purposes.

1. Item Fat Content:

Categorization of the fat content of the grocery items (e.g., Low Fat, Regular). 2. Item Identifier:

Unique identifier for each grocery item in the dataset. 3. Item Type:

Category or type of the grocery item (e.g., Dairy, Frozen Foods, Snacks). 4. Outlet Establishment Year:

Year when the outlet (store) was established. 5. Outlet Identifier:

Unique identifier for each outlet (store) in the dataset. 6. Outlet Location Type:

Type of location where the outlet is situated (e.g., Urban, Rural). 7. Outlet Size:

Size of the outlet (e.g., Small, Medium, High). 8. Outlet Type:

Type of outlet (e.g., Grocery Store, Supermarket). 9. Item Visibility:

Percentage of total display area of the item in the store. 10. Item Weight: - Weight of the item.

1. Sales:

2. Sales of the item in the given time period.

3. Rating:

4. Customer rating or feedback score for the item or the outlet.

Grocery store sales have grown dramatically since the 90’s. Since 1992, sales have more than doubled. The total sales generated by grocery stores in the United States in 2024 amounted to 895.1 billion U.S. dollars. Top Supermarket Chains The U.S. grocery retail market is dominated by chain supermarkets. In 2018 there were around 31,669 chain supermarket locations in the United States, compared to only 6,638 independent supermarkets. The leading American supermarket in terms of sales is the Kroger Company, which owns and operates several smaller supermarket chains across the United States. In 2023, Kroger’s total retail sales reached close to 150 billion U.S. dollars. The runner-up, Albertsons, generated some 77.9 billion U.S. dollars in sales that year. Americans at the Grocery Store Going to the grocery store is a familiar and comforting ritual for many Americans. In 2017, a survey of American households found that 40 percent of Americans make a weekly trip to the grocery store, while some six percent went to the grocery store four to seven times in a week. Although many products on the shelves of U.S. supermarkets claim to have various health benefits or that they were produced or sourced ethically, American consumers are most drawn to food products that claim to be fresh or farm-fresh.

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

The food and grocery segment yielded the highest revenue with over 60 percent of the total sales of the supermarkets across India during 2016, whereas the non-food FMCG products contributed to 33 percent of the total sales.

In the end, you should only measure and look at the numbers that drive action, meaning that the data tells you what you should do next.🥰

Please do upvote if you love the work.♥️🥰 For more related datasets: https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report

Description: This dataset captures sales transactions from a local restaurant near my home. It includes details such as the order ID, date of the transaction, item names (representing various food and beverage items), item types (categorized as Fast-food or Beverages), item prices, quantities ordered, transaction amounts, transaction types (cash, online, or others), the gender of the staff member who received the order, and the time of the sale (Morning, Evening, Afternoon, Night, Midnight). The dataset offers a valuable snapshot of the restaurant's daily operations and customer behavior.

Columns: 1. order_id: a unique identifier for each order. 2. date: date of the transaction. 3. item_name: name of the food. 4. item_type: category of item (Fastfood or Beverages). 5. item_price: price of the item for 1 quantity. 6. Quantity: how much quantity the customer orders. 7. transaction_amount: the total amount paid by customers. 8. transaction_type: payment method (cash, online, others). 9. received_by: gender of the person handling the transaction. 10. time_of_sale: different times of the day (Morning, Evening, Afternoon, Night, Midnight).

Potential Uses: - Analyzing sales trends over time. - Understanding customer preferences for different items. - Evaluating the impact of payment methods on revenue. - Investigating the performance of staff members based on gender. - Exploring the popularity of items at different times of the day.

Makeyourhandsdirtyonit

New Zealand Retail Sales: sa: Supermarket & Grocery Stores data was reported at 5,094.600 NZD mn in Mar 2018. This records an increase from the previous number of 5,049.600 NZD mn for Dec 2017. New Zealand Retail Sales: sa: Supermarket & Grocery Stores data is updated quarterly, averaging 3,287.600 NZD mn from Sep 1995 (Median) to Mar 2018, with 91 observations. The data reached an all-time high of 5,094.600 NZD mn in Mar 2018 and a record low of 1,722.200 NZD mn in Sep 1995. New Zealand Retail Sales: sa: Supermarket & Grocery Stores data remains active status in CEIC and is reported by Statistics New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.H002: Retail Sales: ANZSIC06: Seasonally Adjusted.

Here are a few use cases for this project:

1. Smart Shopping Assistant: Utilize the "Albertsons" computer vision model to create a mobile app or smart glasses integration that helps shoppers easily identify dog food deals in a grocery store. Upon detecting dog-related sales, such as price reductions, quantity sales, or display sales, the model will notify the shopper, making it easier to find the best deals on dog food and related products.

2. Augmented Reality Store Navigation: Integrate the "Albertsons" model into a store's augmented reality (AR) navigation system to guide customers to dog-related deals. By recognizing and highlighting the specific prices and offers for dog food products, shoppers can quickly locate the deals, enhancing the overall shopping experience.

3. Inventory Management and Stock Monitoring: Implement the "Albertsons" computer vision model in a store's inventory management system to automatically track dog products with price reductions, quantity sales, or display sales. This would enable real-time sales data monitoring and streamline the process of updating inventory and restocking when necessary.

4. Personalized Marketing and Promotions: Use the "Albertsons" model to analyze user preferences and behavioral patterns related to dog food purchases. This information can help generate targeted marketing campaigns for dog owners, such as sending personalized notifications, discount vouchers, or product recommendations based on the customer's shopping history and detected sales.

5. Retail Store Analytics and Sales Evaluation: Apply the "Albertsons" computer vision model to study the effectiveness of dog food sale promotions within a retail store. By analyzing the data, store managers can identify patterns and trends, assess the success of different promotions or displays, and adapt their strategies to optimize sales performance for dog-related products.

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

Please do upvote if you love the work.♥️🥰 For more related datasets: https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Aggregated and anonymized purchase data from consumer credit and debit card spending. Spending is reported based on the ZIP code where the cardholder lives, not the ZIP code where transactions occurred. Data from Affinity Solutions, compiled by Opportunity Insights.

Update Frequency: Weekly Date Range: January 13th until the most recent date available.

Data Frequency: Data is daily until the final two weeks of the series, and the daily data is presented as a 7-day lookback moving average. For the final two weeks of the series, the data is weekly and presented as weekly data points.

Index Period: January 4th - January 31st

Indexing Type: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

For dataset column description, please refer to column description

A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

Small, clean dataset for learning purposes.

Data sourced from QSR Magazine, a business-to-business magazine in the quick service restaurant industry. This dataset includes the top 50 fast food chains in the U.S. in 2020. Contains information on the total sales, sales per unit, franchise units, company owned units, and unit change from 2018.

Columns include: - Company Name - Category (pizza, burger, etc) - Sales in Millions (2019) - Sales Per Unit in Thousands (2019) - # of Franchised Units (2019) - # of Company Owned Units (2019) - # of Total Units (2019) - Unit # Change from 2018

This dataset has details of orders placed by customers to the restaurants in a food delivery app. There are 500 orders that were placed on a day.

Join both the tables in this dataset to get the complete data.

You can use dataset to find the patterns in the orders placed by customers. You can analyze this dataset to find the answers to the below questions. 1) Which restaurant received the most orders? 2) Which restaurant saw most sales? 3) Which customer ordered the most? 4) When do customers order more in a day? 5) Which is the most liked cuisine? 6) Which zone has the most sales?

Please upvote if you like my work.

Disclaimer: The names of the customers and restaurants used are only for representational purposes. They do not represent any real life nouns, but are only fictional.

Proportion of agricultural operations selling food locally by food products contributing most to local food sales, by farm size (small, medium and large), 2022.

Here are a few use cases for this project:

1. Retail Inventory Management: The "Sale Detection" model can be used in stores to automate tracking of ongoing sales and discounts in real time, enabling store managers to monitor inventory levels and re-stock items more efficiently.

2. Shopping Assistant Apps: The model can be integrated into shopping apps that help consumers find the best deals and discounts on products by scanning shelves or store displays and detecting sale signs or price reductions.

3. Digital Marketing & Advertising Analysis: The "Sale Detection" model can be used by marketing professionals to analyze the effectiveness of various sales promotions based on customer responses, helping them optimize future marketing campaigns.

4. Dynamic Pricing and Revenue Optimization: E-commerce platforms and online retailers can use the "Sale Detection" model to analyze competitor prices and promotions, allowing them to adjust their own prices and offers dynamically to remain competitive.

5. Customer Behavior Analysis: The model can be used to analyze in-store customer behavior and engagement, such as how much time customers spend in specific sale areas, which sales tactics attract more attention, and what types of sales promotions generate the most customer interactions.

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat