{ "data_id": "43583", "name": "Water-Capture-by-Method", "exact_name": "Water-Capture-by-Method", "version": 1, "version_label": "v1.0", "description": "Context\nThough 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. \nContent\nEach 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.\nMethods\nThis dataset was created using Kaggle's API from this dataset on the City of LA's open data portal:\ncurl -o los_angeles_water_capture.csv https:\/\/data.lacity.org\/resource\/bnhe-q7a5.csv\nkaggle datasets init -p .\nkaggle datasets create -p . \nInspiration\n\nHow much does it rain in Los Angeles?\nWhere does the most rain capture come from?", "format": "arff", "uploader": "Dustin Carrion", "uploader_id": 30123, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-24 00:09:45", "update_comment": null, "last_update": "2022-03-24 00:09:45", "licence": "CC0: Public Domain", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102408\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Water-Capture-by-Method", "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. " ], "weight": 5 }, "qualities": { "NumberOfInstances": 296, "NumberOfFeatures": 7, "NumberOfClasses": null, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 6, "NumberOfSymbolicFeatures": 0, "Dimensionality": 0.02364864864864865, "PercentageOfNumericFeatures": 85.71428571428571, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0, "AutoCorrelation": null, "PercentageOfMissingValues": 0 }, "tags": [ { "uploader": "38960", "tag": "Computer Systems" }, { "uploader": "38960", "tag": "Machine Learning" } ], "features": [ { "name": "barrels_and_cisterns_capture", "index": "0", "type": "numeric", "distinct": "46", "missing": "0", "min": "0", "max": "51", "mean": "33", "stdev": "17" }, { "name": "gi_capture", "index": "1", "type": "numeric", "distinct": "49", "missing": "0", "min": "18158", "max": "40901", "mean": "31680", "stdev": "4762" }, { "name": "incidental_capture", "index": "2", "type": "numeric", "distinct": "71", "missing": "0", "min": "1035", "max": "13476", "mean": "9165", "stdev": "3555" }, { "name": "rain_in", "index": "3", "type": "numeric", "distinct": "45", "missing": "0", "min": "2", "max": "5", "mean": "3", "stdev": "1" }, { "name": "spreading_capture", "index": "4", "type": "numeric", "distinct": "2", "missing": "0", "min": "752", "max": "5234", "mean": "1570", "stdev": "1734" }, { "name": "timestamp", "index": "5", "type": "string", "distinct": "296", "missing": "0" }, { "name": "total_capture", "index": "6", "type": "numeric", "distinct": "73", "missing": "0", "min": "24427", "max": "55180", "mean": "42453", "stdev": "6737" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }