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Waiter Gratuity Prediction Data
Data Science and Analytics
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About
captures recorded data concerning tips provided to waiters by restaurant patrons. The food server tracked key financial and demographic variables associated with dining visits, including the total amount of the bill and customer information. This data is valuable for building machine learning models aimed at predicting tipping behaviour and for conducting analytical studies of service industry economics.
Columns
- total_bill: The entire cost of the dining experience in dollars, inclusive of all taxes. Values range from 3.07 to 50.8, with an average bill amount of 19.8.
- tip: The monetary gratuity given to the waiter in dollars. Values range from 1 to 10, with an average tip amount of 3.
- sex: Indicates the gender of the individual responsible for settling the bill. The dataset shows 64% Male and 36% Female payers.
- smoker: A boolean indicator specifying whether the paying person smoked during the visit or not. Roughly 38% of records indicate a smoker.
- day: Specifies the day of the week when the transaction occurred, covering four unique days, with Saturday being the most frequent day.
- time: Specifies the dining period, categorized as either lunch or dinner. Dinner constitutes 72% of the records.
- size: The total number of people seated at the table, ranging from 1 up to 6 individuals.
Distribution
This dataset is designed for predictive modelling, structured in a tabular format. The data is typically stored in a CSV file, approximating 7.94 kB in size. It consists of 7 descriptive columns and 244 valid records. Notably, there are no missing values (0%) across any of the included fields.
Usage
This data is perfectly suited for developing machine learning models to forecast the expected tip amount given various dining conditions. It can also be used for detailed statistical analysis to determine which factors (e.g., total bill, party size, time of day) are the strongest indicators of gratuity size. Ideal for academic research into consumer behaviour within the hospitality sector.
Coverage
The scope of the data pertains to transactions recorded by a single food server in a restaurant environment. Temporal coverage includes specific days of the week and meal times (lunch and dinner). Demographic details encompass customer gender and smoking status. The expected update frequency for this type of data is annually.
License
CC BY-NC-SA 4.0
Who Can Use It
- Data Science Learners: Excellent as a beginner-level project for regression or classification exercises.
- Business Analysts: Professionals looking to derive insights into tipping trends for budgeting and revenue forecasting in the restaurant industry.
- Researchers: Academics studying service economics, consumer behaviour, and discretionary spending.
Dataset Name Suggestions
- Waiter Gratuity Prediction Data
- Restaurant Tipping Factors Analysis
- Service Industry Tip Modelling
- Hospitality Gratuity Data
Attributes
Original Data Source: Waiter Gratuity Prediction Data
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