Data
ulaanbaatar-weather-2015-2020

ulaanbaatar-weather-2015-2020

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Context You can find a detailed weather data (2015-2020) of Ulaanbaatar, capital city of Mongolia. Content Data is including the timestamps (UTC) and timely basis data of weather related features, such as temperature, wind etc Inspiration Since there are a lot of need to use weather data but not enough free materials, I am sharing this for you guys! Data Description You can find it easily on https://www.wunderground.com/ Enjoy and Upvote 3

20 features

expire_time_gmtnumeric49184 unique values
0 missing
valid_time_gmtnumeric49184 unique values
0 missing
day_indstring2 unique values
0 missing
tempnumeric79 unique values
11 missing
wx_iconnumeric25 unique values
0 missing
icon_extdnumeric62 unique values
0 missing
wx_phrasestring49 unique values
0 missing
dewPtnumeric65 unique values
11 missing
heat_indexnumeric85 unique values
11 missing
rhnumeric88 unique values
11 missing
pressurenumeric91 unique values
4 missing
visnumeric13 unique values
0 missing
wcnumeric126 unique values
28 missing
wdir_cardinalstring18 unique values
17 missing
wspdnumeric20 unique values
17 missing
uv_descstring4 unique values
0 missing
feels_likenumeric132 unique values
27 missing
uv_indexnumeric10 unique values
0 missing
cldsstring5 unique values
1 missing
datestring49184 unique values
0 missing

19 properties

49184
Number of instances (rows) of the dataset.
20
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
138
Number of missing values in the dataset.
33
Number of instances with at least one value missing.
14
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
70
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.
0.07
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.01
Percentage of missing values.

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