About this dataset

Do you love the convenience of being able to drive through and pick up your food without having to wait? Well, you're not alone. According to a new study by Datafiniti, there are over 10,000 fast food restaurants across the United States.

That's a lot of restaurants! But what does that mean for the average person living in America? Well, it means that there are more than enough options for those who want to grab a quick bite on the go. And it also means that there are plenty of opportunities for those who want to open their own fast food restaurant.

So, if you're thinking about starting your own fast food business, or if you're just curious about where the most (and least) fast food options are in America, then this dataset is for you!

How to use the dataset

To find out where the most fast food restaurants are in the United States, you can use this dataset. The dataset includes the restaurant's name, address, city, state, and website. You can use this information to rank cities with the most and least fast food options

Research Ideas

1. Fast food delivery service that delivers from multiple restaurants

2. An app that allows users to find the healthiest fast food options near them

3. A website that ranks cities by their number of fast food restaurants per capita

Acknowledgements

The original source of the data is Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more

Columns

File: Datafiniti_Fast_Food_Restaurants.csv | Column name | Description | |:----------------|:---------------------------------------------------------------------| | dateAdded | The date the restaurant was added to the database. (Date) | | dateAdded | The date the restaurant was added to the database. (Date) | | dateUpdated | The date the restaurant was last updated in the database. (Date) | | dateUpdated | The date the restaurant was last updated in the database. (Date) | | address | The street address of the restaurant. (String) | | address | The street address of the restaurant. (String) | | categories | The category or categories the restaurant is classified as. (String) | | categories | The category or categories the restaurant is classified as. (String) | | city | The city the restaurant is located in. (String) | | city | The city the restaurant is located in. (String) | | country | The country the restaurant is located in. (String) | | country | The country the restaurant is located in. (String) | | keys | The unique identifier for the restaurant. (String) | | keys | The unique identifier for the restaurant. (String) | | latitude | The latitude coordinate of the restaurant. (Float) | | latitude | The latitude coordinate of the restaurant. (Float) | | longitude | The longitude coordinate of the restaurant. (Float) | | longitude | The longitude coordinate of the restaurant. (Float) | | name | The name of the restaurant. (String) | | name | The name of the restaurant. (String) | | postalCode | The postal code of the restaurant. (String) | | postalCode | The postal code of the restaurant. (String) | | province | The province or state the restaurant is located in. (String) | | province | The province or state the restaurant is located in. (String) | | sourceURLs | The source URL of the restaurant. (String) | | sourceURLs | The source URL of the restaurant. (String) | | websites | The website of the restaurant. (String) | | websites | The website of the restaurant. (String) |

File: FastFoodRestaurants.csv | Column name | Description | |:---------------|:-------------------------------------------------------------| | address | The street address of the restaurant. (String) | | address | The street address of the restaurant. (String) | | city | The city the restaurant is located in. (String) | | city | The city the restaurant is located in. (String) | | country | The country the restaurant i...

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!

The Allegheny County Health Department has generated this list of fast food restaurants by exporting all chain restaurants without an alcohol permit from the County’s Fee and Permit System. A chain restaurant defined by the County is any restaurant that has more than one location in the County. Chain restaurants capture both local and national chains (including locally owned national chains) so long as there is one or more establishments in operation within the County.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Laos Number of Restaurant data was reported at 2,360.000 Unit in 2017. This records a decrease from the previous number of 2,969.000 Unit for 2016. Laos Number of Restaurant data is updated yearly, averaging 1,201.000 Unit from Dec 2002 (Median) to 2017, with 16 observations. The data reached an all-time high of 2,969.000 Unit in 2016 and a record low of 334.000 Unit in 2002. Laos Number of Restaurant data remains active status in CEIC and is reported by Lao Statistics Bureau. The data is categorized under Global Database’s Laos – Table LA.Q006: Number of Restaurants.

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!

Xverum’s Store Location Data offers unmatched global coverage of retail, restaurant, and business locations - spanning 230M+ verified POIs across 5000+ commercial categories in over 249 countries.

Whether you're launching a new retail concept, mapping competitor presence, or enriching your analytics platform with real-world business locations - our bulk dataset helps you unlock rich geospatial context.

What’s Included: ➡️ Store Locations & Addresses: Geocoded with latitude/longitude, city, postal code, country. ➡️ Business Metadata: Brand names, categories & subcategories (e.g., Restaurants, Grocery, Clothing). ➡️ Store Details (if available): Website, phone number, operating hours. ➡️ Structured Delivery: Available in .json via S3 bucket or other cloud storage.

🚫 No Foot Traffic or Mobility Data: Clean, static POI data for precise business intelligence use cases.

Use Cases: ✔️ Retail Site Selection & Market Expansion ✔️ Restaurant Chain Mapping & Competitive Benchmarking ✔️ POI Enrichment for Mapping Platforms & Apps ✔️ Real Estate & Urban Planning Analytics ✔️ Location-Based Targeting & Geospatial Analysis

Why Choose Xverum: ✅ 230M+ Store & Business POIs updated regularly ✅ Global coverage across 249+ countries ✅ 5000+ categories from retail and F&B to professional services ✅ Delivered in bulk only - ideal for enterprise data teams ✅ Privacy-compliant (GDPR/CCPA) & ethically sourced

Request your free sample today and discover how Xverum’s store location data can elevate your retail insights, POI mapping, or expansion planning.

Graph and download economic data for Retail Sales: Restaurants and Other Eating Places (MRTSSM7225USN) from Jan 1992 to Aug 2025 about restaurant, retail trade, sales, retail, and USA.

Jordan Number of Employees: Male: Hotel and Restaurants data was reported at 49,320.000 Person in 2016. This records a decrease from the previous number of 50,607.000 Person for 2015. Jordan Number of Employees: Male: Hotel and Restaurants data is updated yearly, averaging 37,863.000 Person from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 50,607.000 Person in 2015 and a record low of 22,126.000 Person in 2002. Jordan Number of Employees: Male: Hotel and Restaurants data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Jordan – Table JO.G014: Number of Employees.

Restaurant for canning a small pre-sampled zipcode list in USA. If you are interested in building a smilar dataset for a different list of locations. Please contact info@barkingdata.com Data can be used to study merchants of justeat, or cuisines or delivery time and cost , review ratings etc.

Data attributes include: "searched_zipcode","searched_lat","searched_lng","searched_address","searched_state","searched_city","searched_metro","is_gh", "latitude","longitude","distance","loc_name","loc_number","url","address","cuisines","delivery_fee_raw","delivery_fee","delivery_time_raw", "delivery_time","service_fee","phone","review_count","review_rating","RunDate","restaurant_tags","delivery_type"

is_gh means grubhub plus. You may be also interested in: Ubereats restaurant data: https://www.kaggle.com/datasets/polartech/ubereats-restaurant-dataset Doordash restaurant data: https://www.kaggle.com/datasets/polartech/doordash-restaurant-data We specialize in web mining and web data harvesting from the world wide web (including mobile apps), we have built 5000+ datasets for researchers, analysts, scholars , retailers, ... Learn more from https://www.barkingdata.com

Price Elasticity Modeling for Restaurant Menus Market Outlook

According to our latest research, the global Price Elasticity Modeling for Restaurant Menus market size reached USD 1.42 billion in 2024. The market is projected to grow at a robust CAGR of 13.7% during the forecast period, reaching USD 4.07 billion by 2033. This significant growth is primarily driven by the increasing adoption of advanced analytics and artificial intelligence in the restaurant industry, as well as a heightened focus on data-driven menu optimization to maximize profitability and customer satisfaction.

The primary growth factor for the Price Elasticity Modeling for Restaurant Menus market is the rapid digital transformation within the global foodservice industry. Restaurants are increasingly leveraging data analytics to gain insights into consumer behavior, optimize pricing strategies, and enhance menu engineering. The proliferation of point-of-sale systems, customer relationship management tools, and integrated restaurant management platforms has resulted in an exponential increase in data availability. This, in turn, has fueled the demand for sophisticated price elasticity modeling solutions that can analyze complex datasets and deliver actionable recommendations for menu pricing. As restaurants face mounting pressure to remain competitive in a dynamic market, the adoption of these advanced models is expected to become standard practice across the industry.

Another major driver is the evolving consumer landscape, characterized by fluctuating demand patterns and heightened price sensitivity. The global economic environment, coupled with the rise of delivery platforms and changing dining preferences, has made it imperative for restaurants to regularly reassess their pricing strategies. Price elasticity modeling empowers operators to accurately forecast customer responses to price changes, enabling more effective revenue management and promotional planning. With the growing emphasis on personalized dining experiences and dynamic pricing, the integration of machine learning and econometric models into menu management is becoming increasingly prevalent. This trend is further reinforced by the need for restaurants to optimize margins and reduce waste in an environment of rising food and labor costs.

Technological advancements in artificial intelligence and machine learning are also reshaping the Price Elasticity Modeling for Restaurant Menus market. The deployment of AI-powered solutions allows restaurants to move beyond traditional linear models and adopt more sophisticated approaches, such as log-linear and non-linear machine learning models. These technologies enable deeper insights into complex demand drivers, cross-item dependencies, and seasonality effects. As a result, restaurants can implement more granular and context-aware pricing strategies that respond dynamically to market changes. The integration of cloud-based analytics platforms further facilitates real-time data processing and remote access, making advanced price elasticity modeling accessible to both large chains and independent operators.

From a regional perspective, North America currently dominates the Price Elasticity Modeling for Restaurant Menus market, accounting for over 38% of global revenue in 2024. This leadership is attributed to the high penetration of digital technologies, a mature restaurant industry, and widespread adoption of data-driven decision-making tools. However, the Asia Pacific region is expected to exhibit the fastest growth during the forecast period, with a projected CAGR of 15.2%. The rapid expansion of the foodservice sector in emerging markets, along with increasing investments in restaurant technology, is driving adoption across the region. Europe also represents a significant market, characterized by strong regulatory frameworks and a growing emphasis on operational efficiency and customer-centric menu design.

Model Type Analysis

The Model Type segment in the Price Elasticity Modeling for Restaurant Menus market encompasses a diverse range of analytical approaches, each offering unique strengths and applications. Linear regression models remain a foundational tool for many restaurants, given their simplicity and ease of implementation. These models are particularly effective for analyzing straightforward relationships between price changes

Geolocet's POI Data spans the entire European continent, offering a wealth of information about Points of Interest in all countries. The extensive database covers a wide spectrum of sectors, providing valuable insights into the retail landscape, healthcare facilities, educational institutions, and much more. Whether seeking insights into markets, healthcare services, or educational access, Geolocet's POI data offers access to comprehensive information.

🔍 Uncover the Essence of Localities

Geolocet's POI Data allows exploration into the unique characteristics of various localities. With information available for more than 2,500 types of Points of Interest (POIs), including businesses, services, and amenities within specific regions, Geolocet provides valuable aggregated insights. Alternatively, for those seeking precise locations, Geolocet can provide the exact coordinates of individual POIs. This granularity offers the flexibility to craft insightful profiles of local communities or pinpoint specific POIs, aiding in tailored strategies and decisions for specific areas.

🌍 Customizable Data Solutions

At Geolocet, we recognize the significance of tailored solutions, which is why our POI Data is entirely customizable to meet your specific requirements. Whether you need data for a single region, or multiple countries, Geolocet's flexible data solutions empower you to select and acquire precisely the information you need.

Tailored Selection: Our platform allows users to choose the sectors and geographic regions that align most closely with their objectives.

Preferred Formats: Data can be received in your preferred formats, whether it's Shapefile, GeoJSON, or any other compatible format.

Moreover, we provide two distinct lists of available attributes to cater to your diverse data needs:

For Customers Requiring Points Data: - ID - Name - Category - Location Latitude - Location Longitude - Address (available for 50% of records) - Phone Number - Email Address - Website - Opening Hours - Brand - Operator - Wheelchair Accessibility - Uber Grid Cell IDs

Please note that data availability within the above list of attributes may vary depending on the POI category.

For Customers Needing Aggregated Data:

• ID of the Area
• Name of the Area
• Number of Clothing Stores
• Number of Restaurants
• Number of Cafes
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• Number of Cinemas
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• Number of Universities
• Number of Hospitals

It's important to emphasize that the attributes listed for the Aggregated datasets serve as examples. Geolocet offers complete flexibility, allowing you to customize attributes to suit your specific needs.

Reach out to Geolocet today to explore how our POI Data can enhance your decision-making processes and provide invaluable insights for your success.

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To maintain current and relevant insights, Geolocet's POI data undergoes regular updates. Our subscription models provide access to the latest information, enabling users to stay ahead in analyses and strategies. Recognizing the importance of up-to-date data in today's fast-paced world, Geolocet supports ongoing data needs.

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Geolocet's POI Data seamlessly integrates with other data offerings, including Administrative Boundaries Spatial Data and Demographic Data. This integration enriches insights and provides a holistic understanding of regions. Combining POI data with administrative boundaries and demographic information empowers data-driven decisions that consider the broader context.

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Geolocet's POI Data goes beyond numbers, uncovering the essence of each locality and understanding its unique characteristics. Whether in retail, healthcare, education, or any other sector, the data equips users with the insights needed to craft informed strategies, optimize resource allocation, and make decisions that resonate with the target audiences.

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Geolocet's Data as a Service (DaaS) offers flexibility tailored to needs. The transparent pricing model ensures cost-efficiency, allowing payment solely for the required data. Whether a startup is exploring a local market or a multinational corporation is analyzing multiple regions, Geolocet provides solutions that align with those objectives.

Contact Geolocet today to explore how the POI Data can elevate decision-making processes and provide valuable insights for success in those endeavors.

United States Retail Sales: FS: ow: Full Service Restaurants data was reported at 27.702 USD bn in May 2018. This records an increase from the previous number of 25.823 USD bn for Apr 2018. United States Retail Sales: FS: ow: Full Service Restaurants data is updated monthly, averaging 14.362 USD bn from Jan 1992 (Median) to May 2018, with 317 observations. The data reached an all-time high of 27.702 USD bn in May 2018 and a record low of 6.675 USD bn in Feb 1993. United States Retail Sales: FS: ow: Full Service Restaurants data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

MIT Restaurant Corpus - CRFs (Conditional Random Fields) Dataset

A Funny Dive into Restaurant Reviews 🥳🍽️

Welcome to MIT Restaurant Corpus - CRF Dataset! If you are someone who loves food, restaurant and all the jargings that come with it, then you are for a treat! (Pun intended! 😉), Let's break it in the most delicious way!

This dataset obtained from MIT Restaurant Corpus (https://sls.csail.mit.edu/downloads/restaurant/) provides valuable restaurant review data for the NER (Named Entity Recognition) functions. With institutions such as ratings, locations and cuisine, it is perfect for the manufacture of CRF models. 🏷️🍴 Let's dive into this rich resource and find out its ability! 📊📍

🍔 What's Inside This Feast?

The MIT Restaurant Corpus is designed to help you understand the intricacies of restaurant reviews and data about restaurants can be pars and classified. It has a set of files that are structured to give you all ingredients required to make CRF (Conditional Random Field) models for NER (Named Entity Recognition). What is served here:

1.**‘sent_train’** 📝: This file contains a collection of sentences. But not just any sentences. These are sentences taken from real - world restaurant reviews! Each sentence is separated by a new line. It is like a dish of text, a sentence at a time.

2.**‘sent_test’** 🍽️: Just like the ‘sent_train’ file, this one contains sentences, but they’re for testing purposes. Think of it as the "taste test" phase of your restaurant review trip. The sentences here help you assess how well your model has learned the art of NER.

3.**‘label_train’** 🏷️: Now here’s where the magic happens. This file holds the NER labels or tags corresponding to each token in the ‘sent_train’ file. So, for every word in a sentence, there is a related label. It helps the model know what is - whether it’s a restaurant name, location, or dish. This review is like a guide to identify the stars of the show!

4.**‘label_test’** 📋: This file is just like ‘label_train’, but for testing. This allows you to verify if your model predictions are with the reality of the restaurant world. Will your model guess that "Burtito Palace" is the name of a restaurant? You will know here!

Therefore, in short, there is a beautiful one-to-one mapping between ‘sent_train’/‘sent_test’ files and ‘label_train’/‘label_test’ files. Each sentence is combined with its NER tag, which makes your model an ideal recipe for training and testing.

🍕 The NER Labels – What are we Tagging?

The real star of this dataset is the NER tags. If you’re thinking, "Okay, but in reality we are trying to identify in these restaurants reviews?" Well, here is the menu of NER label with which you are working:

• Rating ⭐: The stars or ratings (literally) that gives a reviewer to the restaurant. We all know those stars are important when it is to choosing where to eat!
• Amenity 🛋️: Think about that in the form of comfortable extra that comes with a restaurant, such as free Wi-Fi, or a pet-friendly courtyard.
• Location 📍: This tag marks the location of the restaurant. Therefore, when you see "on Fifth Avenue", you know that it points to the place.
• Restaurant_Name 🍴: Ah, place name! Is this "Burger Bonanza" or "Sushi Central"? What this tag recognizes.
• Price 💰: How much are we talking here? The price tag can be called "$ $ $" or "appropriate". We all need to know how different we are about, right?
• Hours ⏰: Because who wants to show in a restaurant when it is closed? It marks the tag opening and closing time.
• Dish 🍲: Which food is being talked about? "Pad Thai" or "Cheeseberger" are your examples.
• Cuisine 🍣: A tag for food type - whether it is Italian, Japanese, or good OL 'American comfort food.

These NER tags help create an understanding of all the data you encounter in a restaurant review. You will be able to easily pull names, prices, ratings, dishes, and more. Talk about a full-recourse data food!

🍤 CRF Model – The Chef’s Special!

Now, once you get your hand on this delicious dataset, what do you do with it? A ** CRF model ** cooking time!🍳

CRF (conditional random field) is a great way to label the sequences of data - such as sentences. Since NER work is about tagging each token (word) in a sentence, CRF models are ideal. They use reference around each word to perform predictions. So, when you were "wonderful for Sushi in Sushi Central!" As the sentence passes in, the model can find out that "Sushi Central" is a Restaurant_Name, and “sushi” is a Dish.

🍜 Features – Spice It Up!

Next, we dive into defines features for CRF model. Features are like secret materials that work your model. You will learn how to define them in the python, so your model can recognize the pattern and make accurate predictions.

...

Approximately placed by ILRI/WRI based on MacMillan Education Ltd. 1993, UNEP/GRID-Nairobi 1998, and RoK 1998 Cautions Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty. Citation MacMillan Education Ltd. 1993. Kenya Traveller's Map, Third Edition. London and Oxford: MacMillan Education Ltd. United Nations Environment Programme (UNEP)/GRID-Nairobi. 1998. Eastern Africa Coastal and Marine Resources Database. Nairobi: UNEP/GRID-Nairobi. Project description online at http://www.unep.org/eafatlas/dbke.htm Republic of Kenya (RoK). 2003. Gazette Notice. 3976. The Hotels and Restaurants (Classification of Hotels and Restaurants) Regulations, 1988. Vol. CV, No. 62. Nairobi: RoK.

Restaurant POS Systems Market size was valued at USD 9.42 Million in 2023 and is projected to reach USD 17.87 Million by 2030, growing at a CAGR of 8.37% during the forecast period 2024-2030.

Global Restaurant POS Systems Market Drivers

The market drivers for the Restaurant POS Systems Market can be influenced by various factors. These may include:

The Hospitality Industry's Digital Transition: Digital technologies are being used by restaurants more and more to improve both their general operations and patron experience. Because they provide data analytics, increase productivity, and streamline procedures, point-of-sale (POS) systems are essential to this digital revolution. The need for improved customer service: Restaurants may now offer their patrons a more streamlined and effective ordering and payment experience thanks to modern POS systems. An improved client experience is facilitated by features like online reservations, smartphone ordering, and self-service kiosks. Integration with Platforms for Online Ordering and Delivery: The need for point-of-sale (POS) systems that can easily interact with online meal delivery services has surged due to their popularity. POS systems that can effectively handle both in-person and online orders are essential for restaurants. Intelligence in business and data analytics: Robust analytics solutions integrated into point-of-sale (POS) systems offer significant insights into customer behavior, preferences, and sales trends. Restaurants use this information to develop focused marketing efforts, optimize their menus, and make educated judgments. Security and Regulatory Compliance: For the restaurant business, adhering to rules like PCI DSS (Payment Card Industry Data Security Standard) is crucial. Restaurants are in high demand for point-of-sale (POS) systems that provide safe payment processing and assist in adhering to industry regulations. Flexibility & Mobility: Mobile point-of-sale (POS) systems are being used by restaurants more and more, enabling employees to take orders and handle payments at the table. This increases productivity and elevates the dining experience in general. Streamlined Operations and Cost Effectiveness: POS systems facilitate the streamlining of a number of restaurant activities, such as staff scheduling, inventory management, and order processing. Cost reductions and an improvement in overall efficiency may result from this. Cloud-Based Programs: Because of their scalability, flexibility, and accessibility, cloud-based point-of-sale (POS) systems appeal to eateries of all kinds. Real-time updates, remote management, and data accessible from several places are made possible by cloud technologies. Growth of Contactless Transactions: The need for contactless payment methods has increased as safety and hygiene have become more important priorities. POS systems that accept a range of payment options, such as mobile wallets and contactless transactions, are becoming more and more common. Innovation and the Competitive Environment: In order to stay ahead of the competition, companies in the market are often launching new features and technology. To obtain a competitive advantage, restaurants are more likely to implement point-of-sale (POS) systems that include the newest features.

Macedonia Number of Enterprises: Hotels and Restaurants data was reported at 4,627.000 Unit in 2016. This records an increase from the previous number of 4,535.000 Unit for 2015. Macedonia Number of Enterprises: Hotels and Restaurants data is updated yearly, averaging 4,240.000 Unit from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 4,627.000 Unit in 2016 and a record low of 2,345.000 Unit in 2004. Macedonia Number of Enterprises: Hotels and Restaurants data remains active status in CEIC and is reported by State Statistical Office of the Republic of Macedonia. The data is categorized under Global Database’s Macedonia – Table MK.O007: Number of Enterprises: by Industry.

The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of restaurant at different places in Bengaluru, aggregate rating of each restaurant, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. These kind of analysis can be done using the data, by studying the factors such as • Location of the restaurant • Approx Price of food • Theme based restaurant or not • Which locality of that city serves that cuisines with maximum number of restaurants • The needs of people who are striving to get the best cuisine of the neighborhood • Is a particular neighborhood famous for its own kind of food.

“Just so that you have a good meal the next time you step out”

The data is accurate to that available on the zomato website until 15 March 2019. The data was scraped from Zomato in two phase. After going through the structure of the website I found that for each neighborhood there are 6-7 category of restaurants viz. Buffet, Cafes, Delivery, Desserts, Dine-out, Drinks & nightlife, Pubs and bars.

Phase I,

In Phase I of extraction only the URL, name and address of the restaurant were extracted which were visible on the front page. The URl's for each of the restaurants on the zomato were recorded in the csv file so that later the data can be extracted individually for each restaurant. This made the extraction process easier and reduced the extra load on my machine. The data for each neighborhood and each category can be found here

Phase II,

In Phase II the recorded data for each restaurant and each category was read and data for each restaurant was scraped individually. 15 variables were scraped in this phase. For each of the neighborhood and for each category their onlineorder, booktable, rate, votes, phone, location, resttype, dishliked, cuisines, approxcost(for two people), reviewslist, menu_item was extracted. See section 5 for more details about the variables.

Acknowledgements The data scraped was entirely for educational purposes only. Note that I don’t claim any copyright for the data. All copyrights for the data is owned by Zomato Media Pvt. Ltd..

Inspiration I was always astonished by how each of the restaurants are able to keep up the pace inspite of that cutting edge competition. And what factors should be kept in mind if someone wants to open new restaurant. Does the demography of an area matters? Does location of a particular type of restaurant also depends on the people living in that area? Does the theme of the restaurant matters? Is a food chain category restaurant likely to have more customers than its counter part? Are any neighborhood similar ? If two neighborhood are similar does that mean these are related or particular group of people live in the neighborhood or these are the places to it? What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. There are infacts dozens of question in my mind. lets try to find out the answer with this dataset.

For detailed discussion of the business problem, please visit this link

Please visit this link to find codebook cum documentation for the data

GITHUB LINk : https://github.com/mohitbhadauria02/Zomato-Dataset-using-Exploratory-Data-Analysis.git

Algeria Gross Output: Hotels, Cafes and Restaurants data was reported at 310,916.900 DZD mn in 2021. This records an increase from the previous number of 241,415.700 DZD mn for 2020. Algeria Gross Output: Hotels, Cafes and Restaurants data is updated yearly, averaging 54,100.300 DZD mn from Dec 1974 (Median) to 2021, with 48 observations. The data reached an all-time high of 372,617.200 DZD mn in 2019 and a record low of 1,003.300 DZD mn in 1974. Algeria Gross Output: Hotels, Cafes and Restaurants data remains active status in CEIC and is reported by National Office of Statistics. The data is categorized under Global Database’s Algeria – Table DZ.A020: GDP: Gross Output.

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.

About Dataset

This dataset provides a comprehensive view of the restaurant scene in the 13 metropolitan areas of India( 900 restaurants) . Researchers, analysts, and food enthusiasts can use this dataset to gain insights into various aspects such as dining and delivery ratings, customer reviews and preferences, popular cuisines, best-selling items, and pricing information across different cities. It enables the exploration of dining patterns, the comparison of restaurants and cuisines between cities, and the identification of trends in the food industry. This dataset serves as a valuable resource for understanding the culinary landscape and making data-driven decisions related to the restaurant business, customer satisfaction, and food choices in these metropolitan areas of India. In this dataset, we have more than 127000 rows and 12 columns, a fairly large dataset. You will be able to get hands-on experience while performing the following tasks and will be able to understand how real-world problem statement analysis is done. In Data Analysis what all things we do

Handling Missing Values Explore numerical features. Explore categorical features. Finding relations between features. You have to perform the following tasks:

Explore the Data

read the dataset understand each feature and write down the details. explore the dataset info, describe and find columns with categories, and numeric columns as well. Data Cleaning:

Deleting redundant columns. Renaming the columns. Dropping duplicates. Cleaning individual columns. Remove the NaN values from the dataset Check for some more Transformations Data Visualization:

find out few insights from dataset

• What is the average dining rating across all restaurants in the dataset
• Which metropolitan area has the highest average delivery rating
• What is the total number of dining votes received by all restaurants in each city
  • How many unique cuisines are represented in the dataset
• Which restaurant has the highest average dining rating in each city
• What is the average price of the best-selling dishes compared to the non-best-selling dishes