{
    "data_id": "46102",
    "name": "FitBit_Steps",
    "exact_name": "FitBit_Steps",
    "version": 1,
    "version_label": null,
    "description": "**Description:**\n\nThe dataset 'minuteStepsNarrow_merged.csv' provides detailed insights into step activity recorded for various users, identified by their unique IDs, over specific minutes of different days. The data has been meticulously compiled to offer a narrow focus on the exact minute-level activity, showcasing the number of steps taken by a user at a given timestamp. The dataset encompasses a compact structure aimed at facilitating analysis on minute-level step count variations across multiple users and times, offering a microscopic view of physical activity patterns.\n\n**Attribute Description:**\n\n1. **Id**: A numeric identifier for each user. The ID is unique to an individual participant in the dataset. Example values include 7086361926, 2347167796.\n2. **ActivityMinute**: The specific date and time when the steps were recorded, detailed down to the minute. The format follows a M\/D\/YYYY H:MM:SS AM\/PM structure, offering precise pinpointing of activity time. Sample timestamps are '4\/7\/2016 4:40:00 PM', '3\/17\/2016 9:21:00 PM'.\n3. **Steps**: Represents the number of steps taken by the user at the given ActivityMinute. This integer value quantifies the physical activity, with examples like 8, 0, 1 indicating the step count.\n\n**Use Case:**\n\nThis dataset is invaluable for a wide range of applications including, but not limited to, health and fitness trend analysis, personalized health monitoring, research in circadian rhythms of physical activity, and development of gamified fitness challenges. By leveraging the granular data provided in 'minuteStepsNarrow_merged.csv', researchers and developers can gain deep insights into minute-level activity patterns, enabling the creation of targeted interventions for promoting physical activity. Additionally, it can serve as a foundational dataset for machine learning models aimed at predicting future physical activity levels based on historical data, thereby supporting personalized fitness and health recommendations.",
    "format": "arff",
    "uploader": "Iwo Godzwon",
    "uploader_id": 39999,
    "visibility": "public",
    "creator": "\"Mobius\"",
    "contributor": "\"None\"",
    "date": "2024-05-31 18:49:29",
    "update_comment": null,
    "last_update": "2024-05-31 18:49:29",
    "licence": "Public Domain (CC0)",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22120546\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "FitBit_Steps",
            "The dataset 'minuteStepsNarrow_merged.csv' provides detailed insights into step activity recorded for various users, identified by their unique IDs, over specific minutes of different days. The data has been meticulously compiled to offer a narrow focus on the exact minute-level activity, showcasing the number of steps taken by a user at a given timestamp. The dataset encompasses a compact structure aimed at facilitating analysis on minute-level step count variations across multiple users and ti "
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        "NumberOfClasses": null,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 1,
        "NumberOfSymbolicFeatures": 1,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 0,
        "AutoCorrelation": null,
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        "Dimensionality": 2.076067098488623e-6,
        "PercentageOfNumericFeatures": 33.33333333333333,
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    "tags": [],
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