Data
Temperature-Readings--IOT-Devices

Temperature-Readings--IOT-Devices

active ARFF GNU Lesser General Public License 3.0 Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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Context This dataset is a small snap ( sample) out of ocean-depth entries in the original dataset, which keeps increasing day by day. The purpose of this dataset is to allow fellow Scientists/ Analysts to play and Find the unfounds. Content This dataset contains the temperature readings from IOT devices installed outside and inside of an anonymous Room (say - admin room). The device was in the alpha testing phase. So, It was uninstalled or shut off several times during the entire reading period ( 28-07-2018 to 08-12-2018). This random interval recordings and few mis-readings ( outliers) makes it more challanging to perform analysis on this data. Let's see, what you can present in the plate out of this messy data. Technical Details: columns = 5 Rows = 97605 id : unique IDs for each reading room_id/id : room id in which device was installed (inside and/or outside) - currently 'admin room' only for example purpose. noted_date : date and time of reading temp : temperature readings out/in : whether reading was taken from device installed inside or outside of room? Acknowledgements I sincerely thank the team working at LimelightIT Research for providing me the device to record the data and helping me throughout the project. Inspiration I have always been curious to know how climate changes day by day, year to year. One way to understnad this is by analysis and understanding the heat index of an area. Temperature data is a small part of it. But, The findings can lead to bigger and more serious inventions and outcomes! From this dataset , it would be intersting to find out: what was the max and min temperature? How outside temperature was related to inside temperature? any relation between the two? What was the variance of temperature for inside and outside room temperature? What is the trend in the data? Can you use Time Series Forecast algo to predict the next scenario? which was the hottest/coolest month ? any warning signals fro climate disaster ? and many more Data Science is all about finding the possibilities and verifying the probabilities! Thanks !

5 features

temp (target)numeric31 unique values
0 missing
idstring97605 unique values
0 missing
room_id/idstring1 unique values
0 missing
noted_datestring27920 unique values
0 missing
out/instring2 unique values
0 missing

19 properties

97606
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
0
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.
1
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
20
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
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.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
-0.23
Average class difference between consecutive instances.
0
Percentage of missing values.

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