3 datasets found

  1. Sleep Cycle & Productivity

    • kaggle.com
    Updated Feb 7, 2025
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    Adil Shamim (2025). Sleep Cycle & Productivity [Dataset]. https://www.kaggle.com/datasets/adilshamim8/sleep-cycle-and-productivity/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 7, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Adil Shamim
    Description

    ๐Ÿ“Š Sleep Cycle & Productivity Dataset Overview

    This dataset tracks sleep habits and their impact on productivity, mood, and stress levels. It includes 5000 records covering multiple individuals across different ages and lifestyles.

    ๐Ÿ“Œ Dataset Details

    Column NameDescription
    DateThe date of data collection
    Person_IDUnique identifier for each individual
    AgeAge of the person (18-60 years)
    GenderMale, Female, or Other
    Sleep Start TimeTime when the person went to bed (in 24-hour format)
    Sleep End TimeTime when the person woke up (in 24-hour format)
    Total Sleep HoursTotal duration of sleep (in hours)
    Sleep QualitySelf-reported sleep quality (scale: 1-10)
    Exercise (mins/day)Minutes spent exercising per day
    Caffeine Intake (mg)Amount of caffeine consumed in mg
    Screen Time Before Bed (mins)Time spent using screens before sleeping
    Work Hours (hrs/day)Total working hours in a day
    Productivity ScoreSelf-reported productivity score (scale: 1-10)
    Mood ScoreSelf-reported mood score (scale: 1-10)
    Stress LevelSelf-reported stress level (scale: 1-10)

    ๐Ÿ” Key Insights from the Dataset

    • Helps analyze the relationship between sleep duration and productivity.
    • Examines how exercise, caffeine, and screen time affect sleep quality.
    • Identifies patterns in stress levels and mood based on sleep habits.
    • Useful for data analysis, machine learning models, and health research.
  2. P

    SHHS Dataset

    • paperswithcode.com
    Updated Feb 17, 2025
    + more versions
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    (2025). SHHS Dataset [Dataset]. https://paperswithcode.com/dataset/shhs
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    Dataset updated
    Feb 17, 2025
    Description

    The Sleep Heart Health Study (SHHS) is a multi-center cohort study implemented by the National Heart Lung & Blood Institute to determine the cardiovascular and other consequences of sleep-disordered breathing. It tests whether sleep-related breathing is associated with an increased risk of coronary heart disease, stroke, all cause mortality, and hypertension. In all, 6,441 men and women aged 40 years and older were enrolled between November 1, 1995 and January 31, 1998 to take part in SHHS Visit 1. During exam cycle 3 (January 2001- June 2003), a second polysomnogram (SHHS Visit 2) was obtained in 3,295 of the participants. CVD Outcomes data were monitored and adjudicated by parent cohorts between baseline and 2011. More than 130 manuscripts have been published investigating predictors and outcomes of sleep disorders.

  3. Workout & Fitness Tracker Dataset

    • kaggle.com
    Updated Feb 8, 2025
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    Adil Shamim (2025). Workout & Fitness Tracker Dataset [Dataset]. https://www.kaggle.com/datasets/adilshamim8/workout-and-fitness-tracker-data/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 8, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Adil Shamim
    Description

    Workout & Fitness Tracker Dataset

    ๐Ÿ“Œ Overview

    This dataset contains 10,000+ records of workout and fitness-related data collected from various fitness apps and devices. It is designed to help analyze and predict workout efficiency based on user activity, health metrics, and lifestyle factors.

    ๐Ÿ“Š Dataset Features

    Column NameDescription
    User IDUnique identifier for each user
    AgeUserโ€™s age (18-60 years)
    GenderMale, Female, Other
    Height (cm)Userโ€™s height in centimeters
    Weight (kg)Userโ€™s weight in kilograms
    Workout TypeType of workout (Cardio, Strength, Yoga, HIIT, Cycling, Running)
    Workout Duration (mins)Total time spent in workout
    Calories BurnedTotal calories burned during workout
    Heart Rate (bpm)Average heart rate during the workout
    Steps TakenNumber of steps recorded (for walking/running workouts)
    Distance (km)Distance covered in kilometers
    Workout IntensityLow, Medium, High
    Sleep HoursHours of sleep before the workout
    Water Intake (liters)Water consumed in liters
    Daily Calories IntakeTotal calories consumed in a day
    Resting Heart Rate (bpm)Heart rate when at rest
    VO2 MaxOxygen consumption capacity (indicator of cardiovascular fitness)
    Body Fat (%)Estimated body fat percentage
    Mood Before WorkoutMood before the workout (Happy, Neutral, Tired, Stressed)
    Mood After WorkoutMood after the workout (Energized, Neutral, Fatigued)

    ๐Ÿ‹๏ธ Potential Use Cases

    • Predicting Workout Efficiency based on different metrics
    • Analyzing the impact of sleep and nutrition on workout performance
    • Finding correlations between heart rate, workout type, and calories burned
    • Developing AI/ML models to suggest personalized workout plans
    • Tracking fitness habits and their effect on mood

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Share
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Email
Click to copy link
Link copied
Close
Cite
Adil Shamim (2025). Sleep Cycle & Productivity [Dataset]. https://www.kaggle.com/datasets/adilshamim8/sleep-cycle-and-productivity/code
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Sleep Cycle & Productivity

Track sleep habits and analyze their effect on productivity.

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Feb 7, 2025
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Adil Shamim
Description

๐Ÿ“Š Sleep Cycle & Productivity Dataset Overview

This dataset tracks sleep habits and their impact on productivity, mood, and stress levels. It includes 5000 records covering multiple individuals across different ages and lifestyles.

๐Ÿ“Œ Dataset Details

Column NameDescription
DateThe date of data collection
Person_IDUnique identifier for each individual
AgeAge of the person (18-60 years)
GenderMale, Female, or Other
Sleep Start TimeTime when the person went to bed (in 24-hour format)
Sleep End TimeTime when the person woke up (in 24-hour format)
Total Sleep HoursTotal duration of sleep (in hours)
Sleep QualitySelf-reported sleep quality (scale: 1-10)
Exercise (mins/day)Minutes spent exercising per day
Caffeine Intake (mg)Amount of caffeine consumed in mg
Screen Time Before Bed (mins)Time spent using screens before sleeping
Work Hours (hrs/day)Total working hours in a day
Productivity ScoreSelf-reported productivity score (scale: 1-10)
Mood ScoreSelf-reported mood score (scale: 1-10)
Stress LevelSelf-reported stress level (scale: 1-10)

๐Ÿ” Key Insights from the Dataset

  • Helps analyze the relationship between sleep duration and productivity.
  • Examines how exercise, caffeine, and screen time affect sleep quality.
  • Identifies patterns in stress levels and mood based on sleep habits.
  • Useful for data analysis, machine learning models, and health research.
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