Dataset of 700,000 restaurants in the United States complete with detailed contact and geolocation data. The database includes multiple data points such as restaurant name, address, phone number, website, email, opening hours, latitude, longitude, and cuisine.

1) Data Introduction • The Restaurant Dataset includes key restaurant-related attributes such as location, average cost, ratings, and the type of dish (target variable) provided with service information for various restaurants worldwide.

2) Data Utilization (1) Restaurant Dataset has characteristics that: • This dataset provides a variety of information, including the restaurant's name, location (country, city, address, latitude and longitude), average cost of meals, calls, table reservations and online delivery, price point, ratings, and vote counts. (2) Restaurant Dataset can be used to: • Cooking Classification Model Development: Using characteristics such as location, price, service, and rating of a restaurant, we can build a machine learning-based cooking type prediction model. • Establish location and marketing strategies: By analyzing regional popular dishes, ratings, and price point data, you can use them to select new restaurant locations and establish customized marketing strategies.

mit_restaurant NER dataset

This dataset provides restaurant inspections, violations, grades and adjudication information

📘 ABOUT

The Enhanced Zomato Dataset provides comprehensive information on restaurants, including user ratings, cuisine types, prices, and geographic details. This enhanced version of the popular Zomato dataset includes carefully cleaned data and newly engineered features to support advanced analytics, trend analysis, and machine learning applications.

It is especially valuable for data scientists, analysts, and machine learning practitioners seeking to build recommendation systems, price predictors, or restaurant review models.

✅ Key Features of the Dataset:

• ✅ Cleaned data with no missing values
• ✅ Contains restaurant, cuisine, city, and pricing information
• ✅ Includes user ratings for both dining and delivery experiences
• ✅ Features engineered columns such as popularity scores and price-per-vote ratios
• ✅ Ready for data visualization, machine learning, and business insights

This dataset is an excellent resource for exploring food industry patterns, building ML models, and performing customer behavior analysis.

📂 FILE INFORMATION

The dataset contains structured records of restaurant details, user ratings, pricing, and engineered features. It was compiled from a public Zomato dataset and enhanced through feature engineering and cleaning techniques.

• File Type: CSV
• Data Rows: 123,657
• Data Fields: Restaurant details, cuisine, city, prices, votes, ratings, and additional engineered features

📊 COLUMNS DESCRIPTION

Local county health departments inspect restaurants and other retail food service establishments to make sure that employees follow safe food handling practices and have adequate kitchen facilities. Keep in mind, inspection reports are snapshots of the food handling at the establishment at the time of inspection – conditions may be different when you visit.

Whatscooking.restaurants

  Overview

This dataset provides detailed information about various restaurants, including their location, cuisine, ratings, and other attributes. It is particularly useful for applications in food and beverage industry analysis, recommendation systems, and geographical studies.

  Dataset Structure

Each record in the dataset represents a single restaurant and contains the following fields:

_id: A unique identifier for the restaurant… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/whatscooking.restaurants.

The Temporary Program, is no longer accepting applications. *Visit Permanent Dining Out website for information: https://www.diningoutnyc.info/ The New York City Open Restaurant is an effort to implement a citywide multi-phase program to expand outdoor seating options for food establishments to promote open space, enhance social distancing, and help them rebound in these difficult economic times. For real time updates on restaurants registered in the program, please visit NYC Open Restaurants dashboard: https://bit.ly/2Z00kn8 ** Please note this Open Restaurant Applications dataset may contain multiple entries (e.g. restaurants submitting 2 or more applications). The Open Restaurants dashboard website containing real time update, noted above, will have fewer total records due to the removal of multiple applications and only list the newest entry.

This data set is a list of the number and proportion of different types of restaurant in each electoral county in the United States. It also contains other socio-economic and public health data.

In the fast-paced world of hospitality, data is essential for success. Our Global Bar & Restaurant POI database offers in-depth information on the locations of the world's top bars and restaurants, providing businesses with a powerful tool for strategic decision-making. Whether you're a restaurant chain, a marketing agency, or a hospitality researcher, our Global Bar & Restaurant database is a valuable resource for making informed decisions.

What You'll Find in the Database:

• Precise Location Data: Full address, geographic coordinates, and neighborhood details for each establishment.

-Visitation Metrics: GDPR-compliant, non-PII foot traffic insights to help you identify the best locations for your next opening.

• Establishment Information: Official name, unique identifier, and type of establishment (bar, restaurant, café, fast-food chain, etc.).

• Operational Status: Whether the establishment is currently open or closed.

• Date Established: Historical context for trend analysis.

• Data Confidence Level: A rating indicating the accuracy of the information.

How You Can Use This Database:

• Market Analysis: Assess the distribution and density of bars and restaurants globally.

• Site Selection: Identify promising locations for new establishments based on demographics, competition, and visitation metrics of nearby establishments.

• Targeted Marketing: Reach customers near specific establishments with personalized offers.

• Competitive Intelligence: Understand the landscape and identify rivals' strategies.

• Supply Chain Optimization: Streamline logistics based on the distribution of your target establishments.

The global restaurant industry was seriously impacted by the coronavirus (COVID-19) pandemic. Social distancing measures and general caution towards public places caused many consumers to dine out less. According to the source, the year-over-year change of seated diners in restaurants worldwide, compared to 2019, was **** percent on August 1, 2022. Has the global online food delivery sector grown due to COVID-19? The market size of the global online food delivery sector was estimated to total ***** billion U.S. dollars in 2022, a figure that is forecast to grow to over *** billion U.S. dollars by 2027. Due to the coronavirus (COVID-19) pandemic, and a subsequent lack of in-house dining, worldwide digital restaurant food delivery grew across various countries from 2019 to 2020. Digital delivery services are defined as meals or snacks ordered via mobile app, internet, or text message. In total, digital restaurant delivery increased ** percent globally, with the United States increasing the most at *** percent. What is the leading restaurant chain worldwide? When looking at the global restaurant landscape, the majority of the biggest brands are quick service restaurants (QSRs). In a 2021 ranking of the most valuable quick service brands worldwide, McDonald's came out on top, reaching a brand value of ***** billion U.S. dollars. Meanwhile, Starbucks was a not so close second place, at approximately **** billion U.S. dollars.

Title: Simulated Point of Sale (POS) Data with Seasonal Trends

Description: This dataset contains simulated Point of Sale (POS) transactions with seasonal trends. It is generated using Faker, a Python library for generating fake data, to mimic the behavior of a retail store over a period of time. The data includes various attributes such as timestamp, customer ID and name, item category and name, quantity, price per item, total price, and payment method.

Features:

Timestamp: The date and time of the transaction. Customer_ID: Unique identifier for each customer. Customer_Name: Name of the customer. Item_Category: Category of the purchased item, influenced by seasonal trends. Item_Name: Name of the purchased item. Quantity: Quantity of the purchased item. Price_Per_Item: Price per unit of the purchased item. Total_Price: Total price of the transaction (quantity * price per item). Payment_Method: Method of payment used for the transaction (Cash, Credit Card, Debit Card). Objective: This dataset can be used for various analytical purposes, including but not limited to:

Analyzing seasonal trends in customer purchasing behavior. Studying the popularity of different item categories across seasons. Predicting future sales based on historical data and seasonal patterns. Evaluating the effectiveness of marketing campaigns or promotions during different seasons. Usage: Data scientists, analysts, and machine learning enthusiasts can utilize this dataset to explore and practice data analysis, visualization, and predictive modeling techniques.

Acknowledgement: The dataset is generated for educational and practice purposes, using Faker library for fake data generation.

The dataset contains every sustained or not yet adjudicated violation citation from every full or special program inspection conducted up to three years prior to the most recent inspection for restaurants and college cafeterias in an active status on the RECORD DATE (date of the data pull). When an inspection results in more than one violation, values for associated fields are repeated for each additional violation record. Establishments are uniquely identified by their CAMIS (record ID) number. Keep in mind that thousands of restaurants start business and go out of business every year; only restaurants in an active status are included in the dataset. Records are also included for each restaurant that has applied for a permit but has not yet been inspected and for inspections resulting in no violations. Establishments with inspection date of 1/1/1900 are new establishments that have not yet received an inspection. Restaurants that received no violations are represented by a single row and coded as having no violations using the ACTION field. Because this dataset is compiled from several large administrative data systems, it contains some illogical values that could be a result of data entry or transfer errors. Data may also be missing. This dataset and the information on the Health Department’s Restaurant Grading website come from the same data source. The Health Department’s Restaurant Grading website is here: http://www1.nyc.gov/site/doh/services/restaurant-grades.page See the data dictionary file in the Attachments section of the OpenData website for a summary of data fields and allowable values.

The source of the data: https://groups.csail.mit.edu/sls/downloads/restaurant/

The Restaurant Establishments dataset contains data for restaurants, bars, schools, hospitals, food trucks, and other food service providers operating in the City of Detroit. By Michigan State Law, these establishments must obtain food service licenses and get inspected regularly to ensure they are following food safety regulations. The data in this dataset is gathered during licensing and inspection processes. Two closely related datasets, Restaurant Inspections and Violations Cited per Restaurant Inspection, have data gathered during food safety inspections. The Detroit Health Department provides this data and is responsible for licensing and inspecting food service establishments in Detroit to ensure the establishments are meeting food safety standards. Establishment records created or updated between August 1, 2016 and the date of the most recent data update are available in this dataset.

This information is derived from inspections of restaurants and other food establishments in Chicago from January 1, 2010 to the present. Inspections are performed by staff from the Chicago Department of Public Health’s Food Protection Program using a standardized procedure. The results of the inspection are inputted into a database, then reviewed and approved by a State of Illinois Licensed Environmental Health Practitioner (LEHP). For descriptions of the data elements included in this set, go to http://bit.ly/tS9IE8
Disclaimer: Attempts have been made to minimize any and all duplicate inspection reports. However, the dataset may still contain such duplicates and the appropriate precautions should be exercised when viewing or analyzing these data. The result of the inspections (pass, pass with conditions or fail) as well as the violations noted are based on the findings identified and reported by the inspector at the time of the inspection, and may not reflect the findings noted at other times. For more information about Food Inspections, go to http://bit.ly/tD91Sb. Data Owner: Chicago Department of Public Health. Time Period: 2010 - Present. Frequency: Data is updated weekly.

These datasets include information about Yelp restaurant reviews for the city of Boston processed from data scraped by BARI. We have generated a list of Boston restaurants by searching all of Boston's zipcodes on Yelp and then verifying that each identified restaurant has an address that falls within Boston's boundaries. YELP.Reviews is a review-level file that contains information about reviews posted on Yelp. YELP.Restaurants is a restaurant-level file that contains information about the restaurants on Yelp. Restaurant data has been aggregated across census tracts to generate YELP.CT, which includes ecometrics that describe neighborhoods in terms of frequency of reviews.

such as standardizing the restaurants' names and removing irrelevant records. The dataset contains restaurants details extracted from Foursquare.com.

Restaurant

## Overview

Restaurant is a dataset for object detection tasks - it contains Hamburguers annotations for 280 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

MealMe offers in-depth restaurant menu data, including prices, from the top 100,000 restaurants across the USA and Canada. Our proprietary technology collects accurate, real-time menu and pricing information, enabling businesses to make data-driven decisions in competitive intelligence, pricing optimization, and market research. With comprehensive coverage that spans major restaurant platforms and chains, MealMe ensures your business has access to the most reliable data to excel in a rapidly evolving industry.

Platforms and Restaurants Covered: MealMe's database includes data from leading restaurant platforms such as UberEats, Postmates, ToastTakeout, SkipTheDishes, Square, Appfront, Olo, TouchBistro, and Clover, as well as direct menu data from major restaurant chains including Raising Cane’s, Panda Express, Popeyes, Burger King, and Subway. This extensive coverage ensures a detailed view of the market, helping businesses monitor trends, pricing, and availability across a broad spectrum of restaurant types and sizes.

Key Features: Comprehensive Menu Data: Access detailed menu information, including item descriptions, categories, sizes, and customizations. Real-Time Pricing: Monitor up-to-date menu prices for accurate competitive analysis. Restaurant-Specific Insights: Analyze individual restaurant chains such as Raising Cane’s and Panda Express, or platforms like UberEats, for market trends and pricing strategies. Cross-Platform Analysis: Compare menu items and pricing across platforms like ToastTakeout, Olo, and SkipTheDishes for a holistic industry view. Regional Data: Understand geographic variations in menu offerings and pricing across the USA and Canada.

Use Cases: Competitive Intelligence: Track menu offerings, pricing strategies, and seasonal trends across platforms like UberEats and Postmates or chains like Popeyes and Subway. Market Research: Identify gaps in the market by analyzing menus and pricing from top restaurants. Pricing Optimization: Use real-time pricing data to inform dynamic pricing strategies and promotions. Trend Monitoring: Stay ahead by tracking popular menu items, regional preferences, and emerging food trends. Platform Analysis: Assess how restaurants perform across delivery platforms such as SkipTheDishes, Olo, and Square. Industries Benefiting from Our Data Restaurant Chains: Optimize menu offerings and pricing strategies with detailed competitor data. Food Delivery Platforms: Benchmark menu pricing and availability across competitive platforms. Market Research Firms: Conduct detailed analyses to identify opportunities and market trends. AI & Analytics Companies: Power recommendation engines and predictive models with robust menu data. Consumer Apps: Enhance app experiences with accurate menu and pricing data. Data Delivery and Integration

MealMe offers flexible integration options to ensure seamless access to our comprehensive menu data. Whether you need bulk exports for in-depth research or real-time updates via API, our solutions are designed to scale with your business needs.

Why Choose MealMe? Extensive Coverage: Menu data from 100,000+ restaurants, including major chains like Burger King and Raising Cane’s. Real-Time Accuracy: Up-to-date pricing and menu details for actionable insights. Customizable Solutions: Tailored datasets to meet your specific business objectives. Proven Expertise: Trusted by top companies for delivering reliable, actionable data. MealMe empowers businesses with the data needed to thrive in a competitive restaurant and food delivery market. For more information or to request a demo, contact us today!