Data
FitBit_Sleep

FitBit_Sleep

active ARFF Public Domain (CC0) Visibility: public Uploaded 31-05-2024 by Iwo Godzwon
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Description: The dataset named "minuteSleep_merged.csv" captures detailed sleep tracking information, segmented into one-minute intervals, for a group of users. It consists of several key attributes that enable a comprehensive analysis of sleep patterns over time. Attribute Description: - Id: A unique numeric identifier for each user in the dataset. Example values include 8378563200 and 4319703577, indicating distinct participants. - date: The specific date and time when the sleep data was recorded, formatted as MM/DD/YYYY HH:MM:SS AM/PM. Sample entries, such as '4/5/2016 3:56:30 AM', represent when a user was detected as sleeping. - value: A binary indicator showing whether the user was asleep (1) or awake (0) during the given minute. Most entries are '1', denoting a minute spent asleep. - logId: A unique identifier for each sleep log entry. This number is critical for distinguishing between different sleep sessions for the same user. Examples include 11304567341 and 11327738294. Use Case: This dataset is invaluable for research in several areas, including sleep studies, healthcare analytics, and wearable technology performance. Analysts can use it to identify sleep patterns, assess sleep quality, and correlate sleep habits with potential health outcomes. Moreover, it's useful for developers testing algorithms on wearable devices that track sleep. By analyzing temporal sleep data, insights into sleep consistency, duration, and disturbances can be gleaned, offering a quantitative basis for improving sleep health recommendations.

4 features

Idnominal23 unique values
0 missing
datestring54523 unique values
0 missing
valuenominal3 unique values
0 missing
logIdnominal556 unique values
0 missing

19 properties

198559
Number of instances (rows) of the dataset.
4
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
3
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
75
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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