Dark Mode
Synthetic Gym Member Activity & Check-in Data
Synthetic Data Generation
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Simulates gym activity across 10 different locations, featuring user details, gym attributes, check-in history, and subscription plan information. This synthetic dataset provides a detailed view of member engagement, workout patterns, and gym operations, making it ideal for analysing customer behaviour and optimising fitness services.
Columns
- user_id: A unique identifier for each gym user.
- first_name: The first name of the user.
- last_name: The last name of the user.
- age: The age of the user.
- gender: The gender of the user (e.g., Male, Female, Non-binary).
- birthdate: The user's date of birth.
- sign_up_date: The date the user registered for their gym membership.
- user_location: The city where the user resides.
- subscription_plan: The user's gym membership plan (e.g., Basic, Pro, Student).
- gym_id: A unique identifier for each gym location.
- location: The city where the gym is located.
- gym_type: The category of the gym (e.g., Premium, Standard, Budget).
- facilities: A list of amenities available at the gym (e.g., Swimming Pool, Sauna).
- checkin_time: The timestamp marking when a user entered the gym.
- checkout_time: The timestamp marking when a user left the gym.
- workout_type: The type of exercise performed during the visit (e.g., Cardio, Weightlifting).
- calories_burned: An estimate of the calories burned during the workout session.
- price_per_month: The monthly cost of a subscription plan in dollars.
- features: A description of the features included in a subscription plan.
Distribution
The data is structured across four CSV files, including one file with approximately 300,000 check-in records. The main check-in history file contains 6 columns.
Usage
Ideal for building predictive models for customer churn, analysing peak gym usage times to optimise staffing, and personalising marketing campaigns based on member workout preferences and subscription tiers. It can also be used for market basket analysis to identify popular combinations of facilities and workout types.
Coverage
- Geographic: The dataset simulates gym activity in 10 real-world city locations, such as New York and Los Angeles.
- Time Range: Check-in and checkout data spans from 1 January 2023 to 16 October 2023.
- Demographic: Includes user data such as age and gender (Male, Female, Non-binary).
License
CC0: Public Domain
Who Can Use It
- Data Scientists: Can use this data to develop and test models predicting gym attendance or member retention.
- Business Analysts: Can analyse trends in gym usage, popular workout times, and the effectiveness of different subscription plans.
- Marketing Professionals: Can segment users based on their activity and demographics to create targeted promotional offers.
- Gym Owners: Can gain insights into operational efficiency, facility usage, and member behaviour to improve services.
Dataset Name Suggestions
- Synthetic Gym Member Activity & Check-in Data
- Gym Usage and Member Analytics Dataset
- Fitness Centre Check-in and Subscription Data
- Simulated Gym Operations and User Behaviour
Attributes
Original Data Source:Synthetic Gym Member Activity & Check-in Data
QUALITY