{
    "data_id": "43746",
    "name": "NYC-Uber-Pickups-with-Weather-and-Holidays",
    "exact_name": "NYC-Uber-Pickups-with-Weather-and-Holidays",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nThis is a forked subset of the Uber Pickups in New York City, enriched with weather, borough, and holidays information.\n\nContent\nI created this dataset for a personal project of exploring and predicting pickups in the area by merging data that intuitively seem possible factors for the analysis. These are:  \n\nUber Pickups in New York City, from 01\/01\/2015 to 30\/06\/2015 (uber-raw-data-janjune-15.csv). (by FiveThirtyEight via kaggle.com)  \nWeather data from National Centers for Environmental Information.  \nLocationID to Borough mapping. (by FiveThirtyEight)  \nNYC public holidays.   \n\nThe main dataset contained over 10 million observations of 4 variables which aggregated per hour and borough, and then joined with the rest of the datasets producing 29,101 observations across 13 variables. These are:  \n\npickup_dt: Time period of the observations. \nborough: NYC's borough.\npickups: Number of pickups for the period.\nspd: Wind speed in miles\/hour.\nvsb: Visibility in Miles to nearest tenth.\ntemp: temperature in Fahrenheit.\ndewp: Dew point in Fahrenheit.\nslp: Sea level pressure.\npcp01: 1-hour liquid precipitation.\npcp06: 6-hour liquid precipitation.\npcp24: 24-hour liquid precipitation.\nsd: Snow depth in inches.\nhday: Being a holiday (Y) or not (N).\n\n\nAcknowledgements \/ Original datasets:\n\nUber Pickups in New York City, from 01\/01\/2015 to 30\/06\/2015 (uber-raw-data-janjune-15.csv). (by FiveThirtyEight via kaggle.com)  \nWeather data from National Centers for Environmental Information.  \nLocationID to Borough mapping. (by FiveThirtyEight)  \n\n(Picture credits: Buck Ennis)",
    "format": "arff",
    "uploader": "Dustin Carrion",
    "uploader_id": 30123,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-24 07:50:50",
    "update_comment": null,
    "last_update": "2022-03-24 07:50:50",
    "licence": "CC0: Public Domain",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102571\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "NYC-Uber-Pickups-with-Weather-and-Holidays",
            "Context This is a forked subset of the Uber Pickups in New York City, enriched with weather, borough, and holidays information. Content I created this dataset for a personal project of exploring and predicting pickups in the area by merging data that intuitively seem possible factors for the analysis. These are: Uber Pickups in New York City, from 01\/01\/2015 to 30\/06\/2015 (uber-raw-data-janjune-15.csv). (by FiveThirtyEight via kaggle.com) Weather data from National Centers for Environmental Info "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 29101,
        "NumberOfFeatures": 13,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 3043,
        "NumberOfInstancesWithMissingValues": 3043,
        "NumberOfNumericFeatures": 10,
        "NumberOfSymbolicFeatures": 0,
        "Dimensionality": 0.0004467200439847428,
        "PercentageOfNumericFeatures": 76.92307692307693,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 10.456685337273633,
        "AutoCorrelation": null,
        "PercentageOfMissingValues": 0.8043604105595104
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Statistics"
        },
        {
            "uploader": "38960",
            "tag": "Transportation"
        }
    ],
    "features": [
        {
            "name": "pickup_dt",
            "index": "0",
            "type": "string",
            "distinct": "4343",
            "missing": "0"
        },
        {
            "name": "borough",
            "index": "1",
            "type": "string",
            "distinct": "6",
            "missing": "3043"
        },
        {
            "name": "pickups",
            "index": "2",
            "type": "numeric",
            "distinct": "3406",
            "missing": "0",
            "min": "0",
            "max": "7883",
            "mean": "490",
            "stdev": "996"
        },
        {
            "name": "spd",
            "index": "3",
            "type": "numeric",
            "distinct": "114",
            "missing": "0",
            "min": "0",
            "max": "21",
            "mean": "6",
            "stdev": "4"
        },
        {
            "name": "vsb",
            "index": "4",
            "type": "numeric",
            "distinct": "179",
            "missing": "0",
            "min": "0",
            "max": "10",
            "mean": "9",
            "stdev": "2"
        },
        {
            "name": "temp",
            "index": "5",
            "type": "numeric",
            "distinct": "295",
            "missing": "0",
            "min": "2",
            "max": "89",
            "mean": "48",
            "stdev": "20"
        },
        {
            "name": "dewp",
            "index": "6",
            "type": "numeric",
            "distinct": "305",
            "missing": "0",
            "min": "-16",
            "max": "73",
            "mean": "31",
            "stdev": "21"
        },
        {
            "name": "slp",
            "index": "7",
            "type": "numeric",
            "distinct": "413",
            "missing": "0",
            "min": "991",
            "max": "1043",
            "mean": "1018",
            "stdev": "8"
        },
        {
            "name": "pcp01",
            "index": "8",
            "type": "numeric",
            "distinct": "80",
            "missing": "0",
            "min": "0",
            "max": "0",
            "mean": "0",
            "stdev": "0"
        },
        {
            "name": "pcp06",
            "index": "9",
            "type": "numeric",
            "distinct": "318",
            "missing": "0",
            "min": "0",
            "max": "1",
            "mean": "0",
            "stdev": "0"
        },
        {
            "name": "pcp24",
            "index": "10",
            "type": "numeric",
            "distinct": "484",
            "missing": "0",
            "min": "0",
            "max": "2",
            "mean": "0",
            "stdev": "0"
        },
        {
            "name": "sd",
            "index": "11",
            "type": "numeric",
            "distinct": "421",
            "missing": "0",
            "min": "0",
            "max": "19",
            "mean": "3",
            "stdev": "5"
        },
        {
            "name": "hday",
            "index": "12",
            "type": "string",
            "distinct": "2",
            "missing": "0"
        }
    ],
    "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
}