Small, clean dataset for learning purposes.

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

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

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

This dataset explores how fast food consumption influences various health and lifestyle indicators. It contains synthetic yet realistic data representing individuals’ eating habits, physical activity, sleep patterns, and overall health outcomes.

The dataset is designed for data analysis and machine learning tasks such as exploratory data analysis (EDA), correlation studies, regression modeling, and classification. It helps uncover patterns between frequent fast food intake and its potential impact on BMI, energy levels, digestive health, and medical visits.

This dataset is beginner-friendly and suitable for students, data analysts, and machine learning practitioners looking to work on health, nutrition, and lifestyle-related projects.

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

1) Data Introduction • The Fast Food Classification Dataset is designed for image classification of various fast food items, consisting of a total of 10 classes: Burger, Donut, Hot Dog, Pizza, Sandwich, Baked Potato, Crispy Chicken, Fries, Taco, and Taquito.

2) Data Utilization (1) Characteristics of the Fast Food Classification Dataset: • The dataset includes images captured under various lighting conditions, backgrounds, and angles, making it suitable for evaluating the generalization performance of classification models in real-world scenarios.

(2) Applications of the Fast Food Classification Dataset: • Training fast food image classification models: Can be used to develop deep learning–based image classifiers that accurately distinguish between various fast food items. • Building automated food recognition systems: Applicable to real-time food identification and classification in self-order kiosks, smart POS systems, and food recognition apps.

Comprehensive nutritional details (calories, total fat, protein, sodium, carbohydrates, saturated fat, trans fat, sugars, cholesterol) for over 23,771 menu items across 9 major fast food chains in 80+ countries.

The market is estimated to reach USD 207,415.5 million in 2025 and is expected to grow to USD 341,089.4 million by 2035, reflecting a compound annual growth rate (CAGR) of 5.1% throughout the assessment period.

Metric	Value
Industry Size (2025E)	USD 207,415.5 million
Industry Value (2035F)	USD 341,089.4 million
CAGR (2025 to 2035)	5.1%

Country wise Outlook

Country	CAGR (2025 to 2035)
USA	5.0%

Country	CAGR (2025 to 2035)
UK	5.2%

Country	CAGR (2025 to 2035)
European Union	5.3%

Country	CAGR (2025 to 2035)
Japan	5.1%

Country	CAGR (2025 to 2035)
South Korea	5.4%

Competitive Outlook

Company Name	Estimated Market Share (%)
McDonald's	18-22%
Yum! Brands	15-19%
Darden Concepts, Inc.	10-14%
Quality Is Our Recipe, LLC	8-12%
Carrols Restaurant Group, Inc.	6-10%
Other Companies (combined)	30-40%

Between October and November of 2020, fast food consumers primarily from the United States were asked about the reasons they ate fast food. Approximately **** percent of those surveyed stated convenience as the leading reason they ate fast food. Meanwhile, the second highest percentage of respondents, **** percent, said it was because it tasted good.

The global fast food market size was valued at over USD 938.16 billion in 2025 and is expected to expand at a CAGR of around 5.3%, surpassing USD 1.57 trillion revenue by 2035, attributed to growing consumption of fast food among the working population.

Potential Applications of the Dataset:

1. Geospatial Information: Precise geographical coordinates for each Walgreens store, enabling accurate mapping and spatial analysis. State-wise and city-wise breakdown of store locations for a comprehensive overview.

2. Store Details: Store addresses, including street name, city, state, and zip code, facilitating easy identification and location-based analysis. Contact information, such as phone numbers, providing a direct link to store management.

3. Operational Attributes: Store opening and closing hours, aiding businesses in strategic planning and market analysis. Services and amenities are available at each location, offering insights into the diverse offerings of Walgreens stores.

4. Historical Data: Historical data on store openings and closures, providing a timeline perspective on Walgreens' expansion and market presence.

5. Demographic Insights: Demographic information of the areas surrounding each store, empowering users to understand the local customer base.

6. Comprehensive and Up-to-Date: Regularly updated to ensure the dataset reflects the latest information on Walgreens store locations and attributes. Detailed data quality checks and verification processes for accuracy and reliability.

The dataset is structured in a flexible format, allowing users to tailor their queries and analyses based on specific criteria and preferences.

The U.S. fast food market was valued at USD 360 billion in 2024 and is estimated to reach USD 522 billion by 2033, registering a CAGR of 4.0% during the forecast period.

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High population density in metropolitan areas, paired with a promising socio-economic situation, made Japan into an early target for the expansion ambitions of the largest American fast food restaurant chains in the world, which entered Japan at the beginning of the 1970s. As of January 2026, McDonald’s was the leading fast food restaurant chain in Japan in terms of facilities, operating over ***** outlets. Other fast food restaurant chains of foreign origin, such as KFC, and Domino’s, have also recorded an extraordinary upward trajectory and are likewise firmly established in the Japanese market. Much of this explosive expansion of the largest chains was made possible by the franchise business model, which allows local operators to use the brand name in exchange for royalties. Fast food restaurant types Japan’s restaurant industry is enormous. This also means that it is highly saturated and incredibly contested. The success of Western-style fast food restaurant chains prompted the emergence of regional competition, as well as diversification in the local fast food industry. Japan’s most prominent native burger brand is MOS Burger, which was established shortly after KFC’s and McDonald’s entry into the Japanese market. The company was also able to aggressively expand and claim a large share of the fast food market for itself. Other than burger shops, the fast food segment in Japan also features bento shops, and pizza restaurants. The leading bento shop brands are Hotto Motto, and Hokka Hokka Tei, while Domino’s and Pizza Hut are the dominating pizza restaurant brands. Financial performance during the COVID-19 period The COVID-19 pandemic was a difficult period for the restaurant industry in Japan, during which the market contracted significantly. However, the impact was very different depending on the industry subsegment. While bars and drinking places, dinner restaurants, and coffee shops recorded abysmal sales revenues, the fast food segment actually grew during the period, proving to be the most resilient among all restaurant segments. One of the reasons for this was that some consumers shifted to food delivery during the worst periods of the pandemic.

The Fast Food Market Report is Segmented by Product Type (Burger and Sandwiches, Pizza, and More), Cuisine Type (Asian, American, and More), Restaurant Type (Kiosks and Vending, Fast-Casual Restaurants, and More), Ordering Channel (Dine-In, Drive-Thru/Takeaway, and More), Outlet Type (Independent Outlets and Chain Outlets), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

The Johns Hopkins Center for a Livable Future (CLF) obtained the food permit list from the Baltimore City Health Department in August 2011, which includes all sites that sell food, such as stores, restaurants and temporary locations such as farmers' market stands and street carts. The restaurants were grouped into three categories, including full service restaurants, fast food chains and carryouts. Carryout and fast food chain restaurants were extracted from the restaurant layer and spatially joined with the 2010 Community Statistical Area (CSA) data layer, provided by BNIA-JFI. The prepared foods density, per 1,000 people, was calculated for each CSA using the CSA's population and the total number of carryout and fast food restaurants, including vendors selling prepared foods in public markets, in each CSA. Source: Johns Hopkins University, Center for a Livable FutureYears Available: 2011, 2013, 2018, 2019

Fast Food Market holds a forecasted revenue of USD 855.17 Bn in 2026 and is likely to cross USD 1,187.35 Bn by 2033 with a steady annual growth rate of 4.8%.

In 2023, the value of the quick-service restaurant market in Latin America was estimated at around ** billion U.S. dollars. By 2030, this market was expected to grow by **** percent.

Market Size statistics on the Fast Food Restaurants industry in the US

This statistic shows the results of a survey conducted by Cint on fast food restaurants visited in Hong Kong in 2018. During the survey, ***** percent of respondents in Hong Kong stated they go to McDonald's.

According to a survey conducted in 2024, roughly ** percent of the population surveyed in Spain had visited a fast-food restaurant in the ** days prior to taking part in the study, which represents an increase versus the previous year. Sales in the fast-food franchise industry alone amount to over two billion euros in the 2010s.