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Global Cuisine Customer Feedback
Product Reviews & Feedback
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset features cuisine and customer ratings from famous restaurants, providing insights into food from five different origins [1]. It is designed for exploratory data analysis (EDA), enabling users to understand customer preferences and behaviour related to dining experiences. The data includes details on customer demographics, their spending habits, preferred cuisines, and their ratings for food and service [1, 2].
Columns
The dataset comprises 15 columns, each offering unique insights into customer demographics and their restaurant experiences [2].
- User ID: A unique identifier for each customer. It ranges from 1 to 200, with all 200 records being valid [2, 3].
- Area code: The area code of the customer's residence. Values range from 101 to 199, with a mean of 141 and 200 valid entries [3, 4].
- Location: Specifies the area and city of customer residence, with "St. George,NY" and "Upper East Side,NY" being common, and 10 unique locations in total [4].
- Gender: Indicates the customer's gender, with 59% male and 41% female respondents among 200 valid entries [4].
- YOB: Represents the Year of Birth of the customer, spanning from 1955 to 2009. The mean year of birth is approximately 1980 (1.98k), with 200 valid records [4, 5].
- Marital Status: Describes the customer's marital status, categorised as Single (50%), Married (43%), or Other (7%). There are 3 unique values across 200 valid entries [5].
- Activity: Denotes the customer's career status, primarily "Student" (60%) or "Professional" (40%) [6].
- Budget: Represents the budget customers may spend at a restaurant, ranging from 1 to 5. The mean budget is 3.81 [6].
- Cuisines: The cuisine type most preferred or liked by customers at restaurants. "Japanese" (18%) and "Filipino" (17%) are among the 7 unique cuisine types listed [6, 7].
- Alcohol: Indicates the frequency of alcohol intake, with "Never" (44%) being the most common, followed by "Often" (31%) [7].
- Smoker: Describes smoking frequency, with "Socially" (36%) and "Often" (35%) being prevalent categories [7].
- Food Rating: The rating customers gave to the restaurant's food, on a scale of 1 to 5. The mean rating is 3.22 [7, 8].
- Service Rating: The rating customers gave to the restaurant's service, also on a scale of 1 to 5. The mean service rating is 3.23 [8].
- Overall Rating: A mean rating calculated from the food and service ratings, ranging from 1 to 5, with a mean of 3.23 [8, 9].
- Often A S: A boolean indicating whether customers often visited the restaurant, with 13% true and 87% false [9].
Distribution
This dataset is provided as a CSV file, specifically named
Cuisine_rating.csv [2, 10]. It has a file size of 17.83 kB and contains 15 columns [2]. The dataset consists of 200 records across all columns, with no missing or mismatched values [3-9]. The expected update frequency for this dataset is never [2].Usage
This dataset is ideal for various analytical purposes, including:
- Visualising food ratings based on cuisine origin and customer gender [2].
- Comparing service and preference ratings to identify trends and correlations [2].
- Analysing ratings based on diverse criteria such as marital status or profession, to understand how different demographic groups evaluate restaurant experiences [2].
- Exploratory data analysis (EDA) to uncover patterns and relationships within restaurant cuisine and customer ratings [1].
Coverage
The dataset primarily focuses on customer ratings of famous restaurants offering food of five different origins [1]. Demographic coverage includes customer gender, year of birth (ranging from 1955 to 2009), marital status, and career activity (student or professional) [4-6]. Geographic scope appears to include areas like St. George, NY, and Upper East Side, NY [4]. The dataset provides insights into customer budget, preferred cuisines (e.g., Japanese, Filipino), and habits (alcohol intake, smoking frequency) [6, 7].
License
CC BY-NC-SA 4.0
Who Can Use It
This dataset is highly suitable for:
- Data analysts and scientists performing exploratory data analysis on consumer behaviour in the restaurant industry [1].
- Market researchers interested in customer preferences, dining habits, and demographic influences on restaurant choices.
- Academics and students conducting studies on food service quality, customer satisfaction, and the impact of personal attributes on ratings [2].
- Businesses seeking to understand their target audience better and identify opportunities for service and cuisine improvement.
Dataset Name Suggestions
- Restaurant Customer Cuisine Ratings
- Diner Preferences and Restaurant Ratings
- Global Cuisine Customer Feedback
- Restaurant Food and Service Evaluation Data
Attributes
Original Data Source: Global Cuisine Customer Feedback
QUALITY