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Restaurant Business Performance Metrics
Food & Beverage Consumption
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About
This dataset contains information about various restaurants and is designed to predict their revenue based on several operational and marketing features. Each row represents a unique restaurant with attributes that influence its financial performance. The primary objective is to enable the prediction of restaurant revenue through the analysis of these influencing factors.
Columns
- Name: The unique name of the restaurant. There are 8368 unique restaurant names.
- Location: Describes where the restaurant is situated, such as Rural, Downtown, or Suburban. Downtown is the most common location, accounting for 34% of entries.
- Cuisine: The type of culinary style offered, for instance, Japanese, Mexican, or Italian. French and American cuisines each represent 17% of the dataset's entries, with six unique cuisine types overall.
- Rating: The restaurant's average rating, on a scale from 3.00 to 5.00. The mean rating is 4.01, with a standard deviation of 0.58.
- Seating Capacity: The number of seats available at the restaurant. Values range from 30 to 90, with a mean of 60.2 and a standard deviation of 17.4.
- Average Meal Price: The average price of a meal at the restaurant, ranging from £25.00 to £76.00. The mean price is £47.9, with a standard deviation of £14.3.
- Marketing Budget: The financial allocation for the restaurant's marketing efforts. Budgets range from £604 to £9,978, with a mean of £3,220 and a standard deviation of £1,820.
- Social Media Followers: The total number of followers the restaurant has on social media platforms. Numbers vary from 5,277 to 104,000, with a mean of 36,200 and a standard deviation of 18,600.
- Chef Experience Years: The years of experience accumulated by the head chef. This ranges from 1 to 19 years, with a mean of 10.1 and a standard deviation of 5.52.
- Number of Reviews: The total quantity of reviews received by the restaurant. This ranges from 50 to 999 reviews, with a mean of 523 and a standard deviation of 277.
- Avg Review Length: The average character length of the reviews. Lengths range from 50 to 300 characters, with a mean of 175 and a standard deviation of 72.
- Ambience Score: A score indicating the overall ambience of the restaurant, on a scale of 1 to 10. The mean score is 5.52, with a standard deviation of 2.58.
- Service Quality Score: A score reflecting the quality of service provided, also on a scale of 1 to 10. The mean score is 5.51, with a standard deviation of 2.59.
- Parking Availability: A binary indicator (Yes/No) of whether parking facilities are available. Parking is available for approximately 50% of the restaurants.
- Weekend Reservations: The number of reservations made for weekends, ranging from 0 to 88. The mean is 29.5, with a standard deviation of 20.
- Weekday Reservations: The number of reservations made for weekdays, also ranging from 0 to 88. The mean is 29.2, with a standard deviation of 20.
- Revenue: The total revenue generated by the restaurant. Values range from £185,000 to £1,530,000, with a mean of £656,000 and a standard deviation of £267,000.
Distribution
This dataset is provided as a CSV file named
restaurant_data.csv, with a file size of 910.55 kB. It is structured as tabular data, with each row representing a distinct restaurant entry. All columns have 8368 valid records, indicating a consistent number of entries across the dataset. The dataset is not expected to be updated.Usage
This dataset is highly suitable for regression analysis, machine learning model training, and other predictive analytics tasks. It can be utilised to understand the various factors influencing restaurant revenue and to develop models for forecasting future earnings.
Coverage
The dataset includes information on restaurant locations such as Rural, Downtown, and Suburban areas. However, no specific geographic regions or countries are detailed. A time range for the data collection is not specified, and the dataset is not expected to receive future updates.
License
CC BY-SA 4.0
Who Can Use It
This dataset is ideal for:
- Data Analysts and Data Scientists seeking to uncover patterns and relationships within restaurant operational data.
- Machine Learning Engineers for developing and testing predictive models for revenue.
- Business Strategists in the hospitality sector for market analysis and operational planning.
- Academic Researchers and Students exploring economic trends or applying quantitative methods in the food service industry.
Dataset Name Suggestions
- Restaurant Revenue Forecasting Data
- Hospitality Earnings Predictor
- Restaurant Business Performance Metrics
- Food Service Revenue Model Dataset
- Global Restaurant Data for Prediction
Attributes
Original Data Source: Restaurant Business Performance Metrics
QUALITY