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Sleep Time , Predictive Behavioural Modelling Dataset (Synthetic)
Mental Health & Wellness
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£199.99
About
The Sleep Time , Predictive Behavioural Modelling Dataset (Synthetic) is designed for educational and research purposes to analyze the relationship between various lifestyle factors and sleep time. The dataset includes anonymized, synthetic data on factors such as physical activity, reading, work hours, caffeine intake, and relaxation time to explore their impact on the duration of sleep.
Dataset Features
- WorkoutTime: The time spent on physical exercise (in hours).
- ReadingTime: The time spent reading (in hours).
- PhoneTime: The time spent on the phone (in hours).
- WorkHours: The number of hours spent working or engaging in work-related activities.
- CaffeineIntake: The amount of caffeine consumed (in mg).
- RelaxationTime: The time spent on relaxation activities (in hours).
- SleepTime: The amount of sleep the participant gets (in hours).
Distribution
Usage
This dataset can be used for the following applications:
- Health Research: Investigate the impact of different lifestyle factors such as work hours, physical activity, and relaxation time on sleep patterns.
- Predictive Modeling: Build machine learning models to predict sleep duration based on lifestyle data.
- Behavioral Studies: Analyze how habits like caffeine intake and phone usage correlate with sleep time and overall well-being.
- Educational Purposes: Provide a dataset for students and researchers to analyze the relationship between daily activities and sleep.
Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It covers a broad spectrum of factors that contribute to sleep time, allowing for comprehensive research and analysis in the health and wellness domain.
License
CC0 (Public Domain)
Who Can Use It
- Health Researchers: To explore correlations between lifestyle behaviors and sleep duration.
- Data Scientists and Machine Learning Practitioners: To develop predictive models for sleep time based on various lifestyle factors.
- Educators and Students: As a resource for studying the relationships between daily activities and sleep in the context of health analytics.
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