Dark Mode
Synthetic Lifestyle and Time Management Insights
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Detailed insights into daily time management and lifestyle habits are captured in this dataset, designed to explore the balance between professional commitments, personal leisure, and physical well-being. By analysing key variables—ranging from sleep patterns and exercise frequency to commute duration and screen time—researchers can investigate correlations between routine behaviours and self-reported productivity levels. This synthetic collection serves as an ideal resource for understanding how various daily factors contribute to overall life satisfaction and efficiency.
Columns
- User ID: Unique numeric identifier for each participant.
- Age: Participant's age, ranging from 18 to 63 years.
- Daily Work Hours: Duration of time spent on professional work activities each day (approx. 3.4 to 9.5 hours).
- Daily Leisure Hours: Time allocated to recreational and leisure activities (approx. 1.7 to 6.4 hours).
- Daily Exercise Minutes: Time spent on physical activities (values range from 6 to 120, implying minutes despite some source ambiguity).
- Daily Sleep Hours: Total sleep duration per 24-hour cycle (ranging from 5.2 to 8.8 hours).
- Productivity Score: Self-assessed rating of productivity on a scale of 0 to 100.
- Screen Time (hours): Daily duration of screen usage, ranging from approx. 2.8 to 7.8 hours.
- Commute Time: Daily time spent travelling to and from work (ranging from 0.6 to 2.5 hours).
Distribution
- Format: CSV
- Size: 85 rows (records) and 9 columns.
- Structure: Structured tabular data with 100% valid entries, zero missing values, and zero mismatched data types across all fields.
- Note: The data is synthetic (fictitious) and intended for educational or analytical simulation.
Usage
- Exploratory Data Analysis (EDA): Visualise relationships between sleep, exercise, and productivity.
- Regression Analysis: Build machine learning models to predict productivity scores based on daily habits.
- Lifestyle Optimisation: Identify optimal routine configurations for maximum efficiency.
- Educational Training: Ideal for beginners practising statistical analysis or data visualisation techniques.
Coverage
- Geographic: Global (implied by general synthetic nature).
- Demographic: Adults aged 18 to 63.
- Completeness: The dataset is fully populated with no missing values.
License
CC0: Public Domain
Who Can Use It
- Data Science Students: For practising regression and correlation analysis.
- Health & Wellness Coaches: To illustrate the theoretical impact of sleep and exercise on output.
- Productivity Researchers: To model ideal work-life balance scenarios.
- HR Analysts: To simulate employee well-being analytics.
Dataset Name Suggestions
- Daily Habits and Productivity Metrics
- Work-Life Balance and Efficiency Data
- Synthetic Lifestyle and Time Management Insights
- Sleep, Exercise, and Productivity Correlations
Attributes
Original Data Source: Synthetic Lifestyle and Time Management Insights
QUALITYLoading...