How many restaurants are in the U.S.? The number of restaurants in the United States reached a total of ******* in Spring 2018. This figure increased from the previous period by a little over two percent. Types of restaurants includedThe two main categories of restaurants that make up the restaurant count are quick service restaurants (QSR’s) and full service restaurants (FSR’S). QSR’s are restaurants that typically serve ‘fast food’ (or food that is prepared quickly) that have minimal table service. Meanwhile, FSR’s are restaurants that are primarily sit-down and have table service, they may also provide delivery or takeout services. Who has the most units? QSR’s include the likes of fast food powerhouses such as McDonald’s, Burger King, Subway and more. Often, QSR’s are chain restaurants; therefore, it is unsurprising that the number of franchised QSR’s in the U.S. far outweighed the number of franchised FSR’s in the U.S. in 2018. Yet, when it comes to chains vs independent restaurants, independents came out on top in terms of number of units in 2018.

U.S.-based casual dining chain Applebee's had *** restaurants in its international markets in 2024. This was ***** restaurants more than the previous year. Applebee's is a subsidiary of the full-service dining company Dine Brands Global.

The MenuPage dataset contains detailed metadata related to restaurant menu pages, including unique identifiers, page numbers, and image attributes. Each row represents a specific page from a restaurant's menu, and the dataset includes information that could be useful for organizing, analyzing, or processing menu images. The data is presented in a tabular format, with each column representing a distinct attribute of the menu pages. The rows represent individual pages, with each page associated with a specific menu and image.

This dataset provides a structured, high-level view of restaurant menu pages, making it valuable for both visual and analytical tasks.

In 2024, the share of operators of drinking establishments in Japan that marked declining customer numbers as a problem amounted to **** percent. This was a wide-spread difficulty encountered in almost all restaurant segments but was especially pronounced among drinking establishments.

About this Dataset

Contact information for over 20,000 restaurants across the US. All restaurants from the NAICS code 72251: Restaurants and Other Eating Places. This includes all set down, fast casual, fast food, and ethnic restaurants. List includes name, address, phone number, website, contact email address, and a brief description. Data was collected from a combination of web scrapping and manual data entry. Similar lists cost over $1500 from lead generation and business data companies.

Category

Lead Generation

Keywords

restaurants,contact,mailing

Row Count

21210

Price

$499.00

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

**Project Name: Swiggy Restaurants Data Analysis

Swiggy is India's Second-largest food delivery app and the candidate is expected to solve following questions**

Problem Statement: How many cities (including subregions) where Swiggy is having its restaurants listed? How many cities (don't include subregions) where Swiggy is having their restaurants listed? The Subregion of Delhi with the maximum number of restaurants listed on Swiggy? Name the top 5 Most Expensive Cities in the Datasets. List out the top 5 Restaurants with Maximum & minimum ratings throughout the dataset. Name of top 5 cities with the highest number of restaurants listed. Top 10 cities as per the number of restaurants listed? Name the top 5 Most Popular Restaurants in Pune. Which SubRegion in Delhi is having the least expensive restaurant in terms of cost? Top 5 most popular restaurant chains in India? Which restaurant in Pune has the most number of people visiting? Top 10 Restaurants with Maximum Ratings in Banglore Top 10 Restaurant in Patna w.r.t rating

Dataset updated:

Feb 14, 2024

Dataset provided by:

data.world, Inc.

Authors:

Datafiniti

Area covered:

North Pacific Ocean, Pacific Ocean

Data Description:

This is a list of 10,000 fast food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more. Note that this is a sample of a large dataset. The full dataset is available through Datafiniti. You can use this data to rank cities with the most and least fast food restaurants across the U.S. E.g.:

Cities with the most and least McDonald's per capita
Fast food restaurants per capita for all states
Fast food restaurants with the most locations nationally
Major cities with the most and least fast food restaurants per capita
Small cities with the most fast food restaurants per capita
States with the most and least fast food restaurants per capita
The number of fast food restaurants per capita

This statistic shows the number of enterprises for restaurants and mobile foodservice activities in Luxembourg from 2007 to 2017. The number of enterprises in the sector has increased in the period under consideration, rising from a total of ***** in 2007 to ***** in 2017. For the equivalent data on the turnover of the overall foodservice sector, please see the following.

This is a list of 10,000 fast food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to rank cities with the most and least fast food restaurants across the U.S. E.g.:

• Cities with the most and least McDonald's per capita
• Fast food restaurants per capita for all states
• Fast food restaurants with the most locations nationally
• Major cities with the most and least fast food restaurants per capita
• Small cities with the most fast food restaurants per capita
• States with the most and least fast food restaurants per capita
• The number of fast food restaurants per capita

Original Data

Acknowlegement

Foto von Haseeb Jamil auf Unsplash

Number of Businesses statistics on the Fast Food Restaurants industry in the US

Over the five years through 2025, restaurant and takeaway revenue is expected to rise at a compound annual rate of 3.7%. France’s culinary heritage is internationally renowned. However, the industry is navigating a shift. Despite numerous Michelin-starred restaurants indicating the quality of France’s food scene, economic headwinds like climbing inflation have dampened consumer spending in recent years, keeping people from dining out as frequently. In response, restaurants are leaning into cost-friendly options like prix fixe menus and casual dining. Fast-food chains, are thriving, riding the wave of demand for affordability and convenience, while bistronomy – where restaurants merge casual formats and gourmet perks – is performing well by targeting value seekers. In 2025, revenue is slated to grow by 3.7% to €58.2 billion. France’s multicultural population is driving demand for diverse flavours. Cities like Paris buzz with international eateries, driven by countries like Peru and Thailand. At the same time, health consciousness and sustainability are reshaping dining habits, driving a surge in demand for plant-based offerings, ethical sourcing and transparent supply chains to capture demand. Profit is expected to have remained steady over the past four years thanks to price adjustments strategies, with rising ingredient costs encouraging restaurants to regularly adjust menu prices to protect their returns. Over the five years through 2030, revenue is expected to swell at a compound annual rate of 1.2% to €61.7 billion. Multicultural flavours will continue to take centre-stage, reflecting France’s multicultural population, as well as appealing to a global audience. Health and environmental consciousness will continue to flourish. As diners prioritise well-being, restaurants will spotlight functional ingredients and cater to niches like veganism and eco-gastronomy, . Casual dining should expand as diners become more financially cautious, presenting growth opportunities for versatile eateries. Digitalisation will also continue to redefine dining experiences, with tools like self-service kiosks, streamlining operations, reducing costs and enhancing customer satisfaction. Meanwhile, it’s a tough choice deciding where to set up base. While Paris dazzles, regions like Vale de Loire, Provence-Alpes-Côte d'Azur and Hauts-de-France offer rich opportunities with growing tourist numbers and vibrant local flavours.

context :

This dataset originates from a study of restaurant tipping behavior. It captures how different factors—such as gender, smoking preference, day of the week, time of day, and group size—affect the amount of tip given by customers. The data provides a simple yet insightful example for learning data analysis, visualization, and statistical modeling. It is commonly used in data science tutorials to demonstrate concepts like correlation, regression, and categorical comparisons using Seaborn or Pandas.

Feature Distribution :

Total Bill: Varies from small amounts to higher restaurant bills, typically ranging between $3 and $50.

Tip: Usually ranges from $1 to $10, showing how customers reward service quality and bill size.

Sex: Two categories — Male and Female.

Smoker: Indicates whether the customer is a smoker (Yes or No).

Day: Includes four days — Thur, Fri, Sat, and Sun (weekdays and weekends).

Time: Divided into Lunch and Dinner, showing when the meal was served.

Size: Represents the number of people dining together, usually between 1 and 6.

Description: This dataset offers a granular, item-level view of daily sales from a diverse group of 50 restaurants, spanning a full year from January 2024 to January 2025. It serves as a comprehensive resource for time-series analysis, demand forecasting, and investigating the various factors that influence customer purchasing habits across different restaurant types, including Cafes, Casual Dining, Fine Dining, Food Stalls, and Kopitiams.

Each row represents the total quantity sold of a specific menu item at a single restaurant on a given day. The dataset is enriched with valuable contextual information, such as weather conditions, promotions, and special events. It also includes detailed financial metrics for each menu item, including the typical ingredient cost, the observed market price, and the actual selling price, making it ideal for analyzing pricing strategies and promotional effectiveness.

Potential Uses: This versatile dataset is well-suited for a variety of analytical projects:

1.**Sales Forecasting**: Develop time-series models (e.g., ARIMA, Prophet) to predict daily sales volumes for specific menu items, individual restaurants, or different restaurant types.

2.**Demand Analysis**: Analyze how external factors like weather conditions and special events impact sales across various item categories and restaurant types.

3.**Promotion Effectiveness**: Evaluate the impact of promotions by comparing the quantity sold and profitability on days with and without promotional activities.

4.**Menu Engineering**: Identify the most and least popular menu items to make data-driven decisions regarding menu optimization, inventory management, and marketing focus. By analyzing item popularity against profitability, restaurants can strategically design their menus to maximize revenue.

5.**Customer Behavior Insights**: Uncover patterns in sales data to understand customer preferences. For instance, determine if certain meal types are more popular on weekends versus weekdays or how pricing impacts choice across different dining segments.

6.**Comparative Analysis**: Compare sales performance, pricing strategies, and menu popularity across different restaurants and restaurant types (e.g., Casual Dining vs. Fine Dining) to identify key drivers of success.

CONTEXT

The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.

The app allows the restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin of the delivery order from the restaurants.

OBJECTIVE

The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company to improve the business.

Context

This dataset is related to a restaurant located at the South of Brasil. This dataset is combining several variables from different nature, such as: Customer, Whether, Sales History, Competitors and more. The idea behind is to aggregate on a singular dataset, all the necessary information to predict the next sales

Wish: - Our current guess is that the algorithms which has better performance are mainly: a regressor, a timeseries and a neural network

Metric: - RMSE with or without cross validation

Context

This data describes about the number of visitors visiting a leading Indian Restaurant chain in the northern part of India. We hope you guys analyse, learn, find, forecast, and find interesting real-world insights from this data.

Content

The actual name of the restaurant has been masked to prevent privacy concerns. All regulatory permissions and considerations have been taken before publishing this dataset. Please note that this dataset is only to be used for academic and research purposes only. Prior consent is required before the commercial use of this data

Acknowledgements

We would like to thank Zomato, Yelp, and the Ministry of Tourism using which this dataset was prepared.

For more information on Zomato API and Zomato API key • Visit : https://developers.zomato.com/api#headline1 • Data Collection: https://developers.zomato.com/documentation

Context

This is a list of 10,000 fast-food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more.

Inspiration

You can use this data to rank cities with the most and least fast-food restaurants across the U.S. E.g.:

1. Cities with the most and least McDonald's per capita
2. Fast food restaurants per capita for all states
3. Fast food restaurants with the most locations nationally
4. Major cities with the most and least fast food restaurants per capita
5. Small cities with the most fast-food restaurants per capita
6. States with the most and least fast food restaurants per capita
7. The number of fast-food restaurants per capita

If you like the dataset, do upvote!

Plastic Restaurant Furniture Market Outlook

The global plastic restaurant furniture market size was valued at $4.2 billion in 2023 and is projected to reach $6.7 billion by 2032, growing at a CAGR of 5.2% during the forecast period. The increasing demand for cost-effective and durable furniture solutions is one of the primary growth factors driving this market.

One of the major growth factors contributing to the expansion of the plastic restaurant furniture market is the growing hospitality industry worldwide. The surge in the number of restaurants, cafes, and fast-food chains is creating a significant demand for affordable and durable furniture. Plastic furniture offers several advantages, such as ease of maintenance, portability, and resistance to weather conditions, making it a preferred choice for both indoor and outdoor settings. Additionally, the trend of themed restaurants and cafes is driving the need for customizable and aesthetically appealing furniture, further boosting the market's growth.

Another significant growth factor is the increasing focus on sustainability and environmentally friendly products. Advances in plastic manufacturing technologies have led to the development of recyclable and eco-friendly plastic furniture. This shift towards sustainable products aligns with the global movement to reduce environmental impact, thus attracting environmentally conscious consumers and businesses. Furthermore, the lower cost of plastic furniture compared to traditional materials like wood or metal makes it an attractive option for budget-conscious restaurant owners.

The rise of e-commerce and online retail platforms is also playing a pivotal role in the growth of the plastic restaurant furniture market. The convenience of online shopping, coupled with the availability of a wide range of products, allows restaurant owners to easily compare and purchase furniture that meets their specific needs. Additionally, online platforms often offer competitive pricing and promotional discounts, further driving the demand for plastic furniture through this distribution channel.

Regionally, Asia Pacific holds a dominant position in the plastic restaurant furniture market. The region's burgeoning middle class and increasing disposable incomes are driving the growth of the hospitality sector, thus boosting the demand for restaurant furniture. Countries like China and India are witnessing rapid urbanization and an increase in the number of dining establishments, contributing significantly to market growth. North America and Europe also present substantial opportunities for market expansion, driven by the growing trend of outdoor dining and the increasing popularity of casual dining restaurants.

Product Type Analysis

The plastic restaurant furniture market is segmented by product type, including chairs, tables, stools, benches, and others. Chairs represent a significant segment of this market due to their ubiquitous presence in any dining setting. The demand for plastic chairs is driven by their lightweight nature, ease of cleaning, and affordability, making them an ideal choice for both high-end restaurants and budget eateries. The ability to stack and store plastic chairs efficiently also adds to their popularity, particularly in establishments with fluctuating patron numbers.

Tables form another crucial segment in the plastic restaurant furniture market. The versatility of plastic tables, which come in various shapes, sizes, and designs, caters to the diverse needs of different dining establishments. Whether it’s a small café or a large banquet hall, plastic tables offer flexibility and durability. Increased outdoor dining options, especially in regions with favorable climates, further drive the demand for weather-resistant plastic tables that can withstand harsh environmental conditions.

Stools and benches, although smaller segments compared to chairs and tables, play a vital role in specific dining scenarios. Stools are often used in bars and casual dining settings where space optimization is essential. The compact and lightweight nature of plastic stools makes them an excellent choice for these environments. Similarly, plastic benches are commonly found in outdoor dining areas, parks, and food courts where they offer communal seating options. The ease of cleaning and maintenance of plastic benches makes them a practical choice for high-tr

Portuguese Restaurant Market Outlook

According to our latest research, the Portuguese restaurant market size reached USD 6.2 billion globally in 2024, reflecting a robust industry driven by rising consumer interest in international cuisines and authentic dining experiences. The market is projected to grow at a CAGR of 6.8% from 2025 to 2033, reaching an estimated value of USD 12.1 billion by 2033. Key growth factors include increasing global tourism, a growing diaspora of Portuguese communities, and a surge in demand for unique culinary experiences that highlight the rich flavors and traditions of Portuguese cuisine.

One of the primary growth drivers for the Portuguese restaurant market is the growing global appetite for ethnic and authentic food experiences. Diners are increasingly seeking out new culinary adventures, and Portuguese cuisine, with its distinctive flavors, diverse seafood dishes, and rich desserts, is gaining popularity worldwide. This trend is further bolstered by the proliferation of food tourism, where travelers aim to immerse themselves in local cultures through gastronomy. The rise of social media has also played a significant role, as visually appealing Portuguese dishes such as bacalhau, pastel de nata, and piri-piri chicken attract food enthusiasts and influencers, further amplifying demand and awareness. Additionally, the expansion of Portuguese restaurants into new markets, particularly in North America and Asia Pacific, is broadening the customer base and fueling market growth.

Another significant factor contributing to the expansion of the Portuguese restaurant market is the increasing presence of Portuguese expatriate communities in various regions, especially in Europe and North America. These communities often seek out authentic flavors from home, supporting the establishment and sustainability of Portuguese eateries. Moreover, the growing trend of fusion cuisine has encouraged innovative chefs to incorporate Portuguese ingredients and techniques into their menus, appealing to a broader audience. The adaptability of Portuguese cuisine to cater to different dietary preferences, including vegetarian and gluten-free options, has also allowed these restaurants to attract a diverse clientele. Furthermore, the increasing number of Portuguese-themed events, festivals, and culinary competitions has heightened interest in the cuisine, fostering greater market penetration.

The evolving landscape of food delivery and takeaway services is another catalyst for growth in the Portuguese restaurant market. With the advent of digital platforms and food delivery apps, Portuguese restaurants can now reach customers beyond their immediate geographic locations, enhancing convenience and accessibility. This shift has proven especially beneficial during periods of restricted dine-in operations, such as during the COVID-19 pandemic, when many restaurants pivoted to online ordering and delivery models. The integration of technology in restaurant operations, from digital menus to contactless payments, has also improved operational efficiency and customer satisfaction. As a result, Portuguese restaurants are better equipped to meet the changing preferences of modern consumers, contributing to sustained market growth.

Regionally, Europe continues to dominate the Portuguese restaurant market, accounting for the largest share in 2024, driven by the strong cultural ties to Portugal and the popularity of Portuguese cuisine among both locals and tourists. However, North America and Asia Pacific are emerging as high-growth regions, with increasing numbers of Portuguese restaurants opening in major metropolitan areas. The Middle East & Africa and Latin America are also witnessing gradual growth, supported by expanding expatriate communities and the rising popularity of international cuisines. This regional diversification is expected to play a crucial role in the market's future trajectory, ensuring steady growth across multiple geographies.

Type Analysis

The Portuguese restaurant market is segmented by type into Casual Dining, Fine Dining, Quick Service Restaurants (QSRs), and Others, each catering to distinct consumer preferences and dining occasions. Casual dining establishments form the backbone of this segment, offering a relaxed ambiance and moderately priced menus that appeal to families, groups of friends, and everyday diners. These rest