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Restaurant Customer Satisfaction Prediction Dataset
Data Science and Analytics
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About
This dataset is designed for predictive modelling and analytics in the hospitality industry, focusing on factors that influence customer satisfaction in restaurants. It offers valuable information on customer visits, including demographic details, specific visit metrics, and customer satisfaction ratings. The dataset aims to facilitate the analysis and prediction of customer satisfaction, helping to identify key drivers and areas for improvement. It is a synthetic dataset, generated for educational purposes, making it ideal for data science and machine learning projects.
Columns
- CustomerID: A unique identifier for each customer.
- Age: The age of the customer.
- Gender: The customer's gender (Male/Female).
- Income: The customer's annual income in USD.
- VisitFrequency: How often the customer visits the restaurant (Daily, Weekly, Monthly, Rarely).
- AverageSpend: The average amount spent by the customer per visit in USD.
- PreferredCuisine: The type of cuisine preferred by the customer (Italian, Chinese, Indian, Mexican, American).
- TimeOfVisit: The usual time of day the customer visits (Breakfast, Lunch, Dinner).
- GroupSize: The number of people in the customer's group during the visit.
- DiningOccasion: The occasion for dining (Casual, Business, Celebration).
- MealType: The type of meal (Dine-in, Takeaway).
- OnlineReservation: Indicates whether the customer made an online reservation (0: No, 1: Yes).
- DeliveryOrder: Indicates whether the customer ordered delivery (0: No, 1: Yes).
- LoyaltyProgramMember: Indicates whether the customer is a member of the restaurant's loyalty program (0: No, 1: Yes).
- WaitTime: The average wait time for the customer in minutes.
- ServiceRating: Customer's rating of the service (on a scale of 1 to 5).
- FoodRating: Customer's rating of the food (on a scale of 1 to 5).
- AmbianceRating: Customer's rating of the restaurant ambiance (on a scale of 1 to 5).
- HighSatisfaction: A binary target variable indicating whether the customer is highly satisfied (1) or not (0).
Distribution
The dataset is provided as a CSV file, named
restaurant_customer_satisfaction.csv, and has a size of 170.9 kB. It contains 19 columns and 1500 records (rows). All columns have 1500 valid entries with no missing data. For instance, customer ages range from 18 to 69, with a mean of 43.8. Gender distribution is 51% Female and 49% Male. Annual incomes vary from £20,012 to £149,875. Visit frequency sees Weekly visits as the most common at 40%, followed by Monthly at 29%. Average spend ranges from £10.31 to £199.97.Usage
This dataset is ideal for a variety of applications and use cases, including:
- Predictive modelling of customer satisfaction.
- Analysing factors that drive customer loyalty and satisfaction.
- Identifying key areas for improvement across service, food, and ambiance.
- Optimising marketing strategies to attract and retain satisfied customers.
- Developing machine learning projects for classification and regression tasks.
- Performing customer segmentation to understand different customer groups.
- Supporting strategic planning within the hospitality sector.
Coverage
The dataset focuses on demographic information (Age, Gender, Income) and visit-specific variables relevant to restaurant customers. It does not explicitly state specific geographic or time range coverage, making it broadly applicable for understanding general patterns in restaurant customer satisfaction. All data within the dataset is synthetic and was generated for educational purposes.
License
Attribution 4.0 International (CC BY 4.0) license
Who Can Use It
This dataset is particularly suitable for data scientists and hospitality analysts. They can leverage it to explore and model customer satisfaction within the restaurant industry, engage in machine learning projects, conduct customer segmentation, and inform strategic planning initiatives.
Dataset Name Suggestions
- Restaurant Customer Satisfaction Prediction Dataset
- Hospitality Experience Analytics
- Diner Feedback Insights
- Customer Loyalty in Restaurants
- Restaurant Service Performance Data
Attributes
Original Data Source: Restaurant Customer Satisfaction Prediction Dataset
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