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Simulated Sleep Factor Dataset
Mental Health & Wellness
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About
This dataset is designed for machine learning models to predict an individual's sleep duration based on various daily lifestyle parameters. It includes features such as time spent on exercise, reading, phone usage, work, caffeine consumption, and relaxation, with sleep time as the target variable. The dataset was generated based on a mathematical equation simulating how these lifestyle factors might influence sleep duration, and it intentionally incorporates outliers to help create models that are robust to noisy real-world data. It serves as a tool for exploring the potential impact of lifestyle choices on sleep patterns, training regression models, evaluating their performance, and fine-tuning sleep prediction algorithms for use in health and wellness applications. It is important to note that this dataset is synthetically generated and does not contain any real-world data; consequently, the features and target variable do not exhibit any meaningful relationships derived from actual observations.
Columns
- WorkoutTime (hours/day): The daily duration an individual spends exercising.
- ReadingTime (hours/day): The daily duration an individual dedicates to reading.
- PhoneTime (hours/day): The daily duration an individual spends using their phone.
- WorkHours (hours/day): The daily hours an individual spends working.
- CaffeineIntake (mg/day): The daily amount of caffeine consumed by an individual.
- RelaxationTime (hours/day): The daily duration an individual spends relaxing, which may include activities like meditation or other leisure pursuits.
- SleepTime (hours/night): The total hours of sleep an individual gets per night.
Distribution
The dataset is provided in CSV format and contains 7 columns. It consists of 2000 records or rows. The file size is approximately 71.85 kB. Each column has a 100% valid data rate with no missing or mismatched values.
Usage
This dataset is ideal for:
- Developing and evaluating machine learning models focused on predicting sleep duration.
- Exploring theoretical impacts of lifestyle choices on sleep patterns.
- Training and fine-tuning regression models for health and wellness applications.
- Researching the correlation between daily activities and sleep requirements.
Coverage
This dataset is synthetically generated using a Python script and does not represent real-world data. Therefore, there is no specific geographic, time range, or demographic scope associated with the data. It is a simulated dataset designed for model training and analysis.
License
CC0: Public Domain
Who Can Use It
This dataset is suitable for:
- Data Scientists: For building and testing predictive models.
- Machine Learning Engineers: For developing and refining algorithms related to health and wellness.
- Researchers: For studying simulated relationships between lifestyle factors and sleep.
- Students: For educational purposes in data analysis and machine learning.
Dataset Name Suggestions
- Sleep Time Prediction Dataset
- Lifestyle Sleep Predictor Data
- Daily Habits Sleep Duration Data
- Simulated Sleep Factor Dataset
Attributes
Original Data Source: Simulated Sleep Factor Dataset
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