Data
Water-Capture-by-Method

Water-Capture-by-Method

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Context Though it doesn't rain often in Los Angeles, the city has various means of capturing rainfall to increase our local water supply. This dataset shows how much water we've capturing cumulatively this season as well as today. Content Each row in this dataset corresponds to a datetime at which a measurement was made. Measurements include water captured in rain barrels and cisterns, incidental capture, green infrastructure capture, etc. For more details, click the "Data" tab of this dataset. Methods This dataset was created using Kaggle's API from this dataset on the City of LA's open data portal: curl -o los_angeles_water_capture.csv https://data.lacity.org/resource/bnhe-q7a5.csv kaggle datasets init -p . kaggle datasets create -p . Inspiration How much does it rain in Los Angeles? Where does the most rain capture come from?

7 features

barrels_and_cisterns_capturenumeric46 unique values
0 missing
gi_capturenumeric49 unique values
0 missing
incidental_capturenumeric71 unique values
0 missing
rain_innumeric45 unique values
0 missing
spreading_capturenumeric2 unique values
0 missing
timestampstring296 unique values
0 missing
total_capturenumeric73 unique values
0 missing

19 properties

296
Number of instances (rows) of the dataset.
7
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.
6
Number of numeric attributes.
0
Number of nominal attributes.
0.02
Number of attributes divided by the number of instances.
85.71
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.
Average class difference between consecutive instances.
0
Percentage of missing values.

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