mit_restaurant NER dataset

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.

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.

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.

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

The Restaurant-ACOS dataset is constructed based on the SemEval 2016 Restaurant dataset (Pontiki et al., 2016) and its expansion datasets (Fan et al., 2019; Xu et al., 2020). The SemEval 2016 Restaurant dataset (Pontiki et al., 2016) was annotated with explicit and implicit aspects, categories, and sentiment. (Fan et al., 2019; Xu et al., 2020) further added the opinion annotations. We integrate their annotations to construct aspect-category-opinion-sentiment quadruples and further annotate the implicit opinions. The Restaurant-ACOS dataset contains 2286 sentences with 3658 quadruples. It is worth noting that the Restaurant-ACOS is available for all subtasks in ABSA, including aspect-based sentiment classification, aspect-sentiment pair extraction, aspect-opinion pair extraction, aspect-opinion sentiment triple extraction, aspect-category-sentiment triple extraction, etc.

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.

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.

Data includes reviews of different restaurants on Google Maps. There are 1100 comments in total and pictures of each comment in the data set. The data is labeled according to 4 classes (Taste, Menu, Indoor atmosphere, Outdoor atmosphere) for the artificial intelligence to predict. The dataset has been prepared in a way that can be used in both text processing and image processing fields.

The dataset contains the following columns: business_name, author_name, text, photo, rating, rating_category

IMPORTANT: The rating_category column is related to the photo of the review. If you want to use this dataset for NLP, you need to label it yourself. I will label it for you when I am available.

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.

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.

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.

Graph and download economic data for Retail Sales: Restaurants and Other Eating Places (MRTSMPCSM7225USN) from Feb 1992 to Apr 2025 about restaurant, retail trade, sales, retail, and USA.

This dataset contains Environmental Health Inspection Results for Restaurants and Markets in the City of Los Angeles. Los Angeles County Environmental Health is responsible for inspections and enforcement activities for all unincorporated areas and 85 of the 88 cities in the County. This dataset combines some of the fields from the County's inspection and violation data, and is filtered to include only facilities in the City of Los Angeles. The full datasets can be found at the following urls: https://data.lacounty.gov/Health/LOS-ANGELES-COUNTY-RESTAURANT-AND-MARKET-INSPECTIO/6ni6-h5kp https://data.lacounty.gov/Health/LOS-ANGELES-COUNTY-RESTAURANT-AND-MARKET-VIOLATION/8jyd-4pv9

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.

The American Customer Satisfaction Index (ACSI) scores for full service restaurants in the United States remained relatively consistent from 2007 to 2024. In 2024, the ACSI score for full service restaurants in the U.S. was *************.

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!

A 2024 survey found that casual dining was the most popular type of dine-in restaurant in the United States, with 69 percent of respondents favoring it. Fast food and fast casual restaurants followed, each preferred by 55 percent of respondents.