Data
KNYC-Metars-2016

KNYC-Metars-2016

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Context METAR is a format for reporting weather information. A METAR weather report is predominantly used by pilots in fulfillment of a part of a pre-flight weather briefing, and by meteorologists, who use aggregated METAR information to assist in weather forecasting. Content This is the METARs aggregated information for 2016 in KNYC. Acknowledgements Thanks to wunderground for providing the data Inspiration This dataset is ment to be used as a extra information for those willing to extract conclusions from their own dataset where hourly the weather information could be useful for their predictions / analysis. You can contact me if you have any doubt or suggestion.

13 features

Timestring8787 unique values
0 missing
Temp.numeric139 unique values
0 missing
Windchillnumeric213 unique values
6492 missing
Heat_Index (ignore)numeric107 unique values
7972 missing
Humiditynumeric89 unique values
0 missing
Pressurenumeric463 unique values
231 missing
Dew_Pointnumeric143 unique values
0 missing
Visibilitynumeric17 unique values
237 missing
Wind_Dirstring18 unique values
0 missing
Wind_Speednumeric22 unique values
0 missing
Gust_Speednumeric26 unique values
0 missing
Precipnumeric42 unique values
0 missing
Eventsstring6 unique values
0 missing
Conditionsstring16 unique values
0 missing

19 properties

8787
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
6960
Number of missing values in the dataset.
6492
Number of instances with at least one value missing.
9
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
69.23
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.
73.88
Percentage of instances having missing values.
Average class difference between consecutive instances.
6.09
Percentage of missing values.

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