{
    "data_id": "44043",
    "name": "nyc-taxi-green-dec-2016",
    "exact_name": "nyc-taxi-green-dec-2016",
    "version": 6,
    "version_label": null,
    "description": "Dataset used in the tabular data benchmark https:\/\/github.com\/LeoGrin\/tabular-benchmark,  \n                                  transformed in the same way. This dataset belongs to the \"regression on categorical and\n                                  numerical features\" benchmark. Original description: \n \nTrip Record Data provided by the New York City Taxi and Limousine Commission (TLC) [http:\/\/www.nyc.gov\/html\/tlc\/html\/about\/trip_record_data.shtml]. The dataset includes TLC trips of the green line in December 2016. Data was downloaded on 03.11.2018. For a description of all variables in the dataset checkout the TLC homepage [http:\/\/www.nyc.gov\/html\/tlc\/downloads\/pdf\/data_dictionary_trip_records_green.pdf]. The variable 'tip_amount' was chosen as target variable. The variable 'total_amount' is ignored by default, otherwise the target could be predicted deterministically. The date variables 'lpep_pickup_datetime' and 'lpep_dropoff_datetime' (ignored by default) could be used to compute additional time features. In this version, we chose only trips with 'payment_type' == 1 (credit card), as tips are not included for most other payment types. We also removed the variables 'trip_distance' and 'fare_amount' to increase the importance of the categorical features 'PULocationID' and 'DOLocationID'.",
    "format": "arff",
    "uploader": "Leo Grin",
    "uploader_id": 26324,
    "visibility": "public",
    "creator": null,
    "contributor": "\"Leo Grin\"",
    "date": "2022-06-18 13:08:59",
    "update_comment": null,
    "last_update": "2022-06-18 13:08:59",
    "licence": "Public",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/old.openml.org\/data\/download\/22103131\/dataset",
    "default_target_attribute": "tip_amount",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "nyc-taxi-green-dec-2016",
            "Dataset used in the tabular data benchmark https:\/\/github.com\/LeoGrin\/tabular-benchmark, transformed in the same way. This dataset belongs to the \"regression on categorical and numerical features\" benchmark. Original description: Trip Record Data provided by the New York City Taxi and Limousine Commission (TLC) [http:\/\/www.nyc.gov\/html\/tlc\/html\/about\/trip_record_data.shtml]. The dataset includes TLC trips of the green line in December 2016. Data was downloaded on 03.11.2018. For a description of "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 581835,
        "NumberOfFeatures": 11,
        "NumberOfClasses": 0,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 4,
        "NumberOfSymbolicFeatures": 7,
        "AutoCorrelation": 0.3560496958964945,
        "PercentageOfMissingValues": 0,
        "Dimensionality": 1.8905703507008002e-5,
        "PercentageOfNumericFeatures": 36.36363636363637,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 63.63636363636363,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 3,
        "PercentageOfBinaryFeatures": 27.27272727272727,
        "PercentageOfInstancesWithMissingValues": 0
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    "tags": [
        {
            "uploader": "38960",
            "tag": "Computer Systems"
        },
        {
            "uploader": "38960",
            "tag": "Machine Learning"
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    ],
    "features": [
        {
            "name": "tip_amount",
            "index": "10",
            "type": "numeric",
            "distinct": "1811",
            "missing": "0",
            "target": "1",
            "min": "-2",
            "max": "6",
            "mean": "1",
            "stdev": "1"
        },
        {
            "name": "VendorID",
            "index": "0",
            "type": "nominal",
            "distinct": "2",
            "missing": "0",
            "distr": []
        },
        {
            "name": "store_and_fwd_flag",
            "index": "1",
            "type": "nominal",
            "distinct": "2",
            "missing": "0",
            "distr": []
        },
        {
            "name": "RatecodeID",
            "index": "2",
            "type": "nominal",
            "distinct": "5",
            "missing": "0",
            "distr": []
        },
        {
            "name": "passenger_count",
            "index": "3",
            "type": "numeric",
            "distinct": "10",
            "missing": "0",
            "min": "0",
            "max": "9",
            "mean": "1",
            "stdev": "1"
        },
        {
            "name": "extra",
            "index": "4",
            "type": "nominal",
            "distinct": "5",
            "missing": "0",
            "distr": []
        },
        {
            "name": "mta_tax",
            "index": "5",
            "type": "nominal",
            "distinct": "3",
            "missing": "0",
            "distr": []
        },
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            "name": "tolls_amount",
            "index": "6",
            "type": "numeric",
            "distinct": "105",
            "missing": "0",
            "min": "0",
            "max": "98",
            "mean": "0",
            "stdev": "1"
        },
        {
            "name": "improvement_surcharge",
            "index": "7",
            "type": "nominal",
            "distinct": "3",
            "missing": "0",
            "distr": []
        },
        {
            "name": "total_amount",
            "index": "8",
            "type": "numeric",
            "distinct": "5377",
            "missing": "0",
            "min": "-63",
            "max": "712",
            "mean": "17",
            "stdev": "12"
        },
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            "name": "trip_type",
            "index": "9",
            "type": "nominal",
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            "missing": "0",
            "distr": []
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    "nr_of_issues": 0,
    "nr_of_downvotes": 0,
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    "total_downloads": 0,
    "reach": 0,
    "reuse": 0,
    "impact_of_reuse": 0,
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    "impact": 0
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