{
    "data_id": "45043",
    "name": "seattlecrime6",
    "exact_name": "seattlecrime6",
    "version": 3,
    "version_label": null,
    "description": "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 both numerical and categorical features\" benchmark. \n \n  Original link: https:\/\/openml.org\/d\/42496 \n \n Original description: \n \n**Author**: City of Seattle\n**Source**: https:\/\/data.seattle.gov\/Public-Safety\/Crime-Data\/4fs7-3vj5 - 24-06-2019\n**Please cite**:   \n\nThis data represents crime reported to the Seattle Police Department (SPD). Each row contains the record of a unique event where at least one criminal offense was reported by a member of the community or detected by an officer in the field. This data is the same data used in meetings such as SeaStat (https:\/\/www.seattle.gov\/police\/information-and-data\/seastat) for strategic planning, accountability and performance management. \n\nFor more information see:\nhttps:\/\/data.seattle.gov\/Public-Safety\/Crime-Data\/4fs7-3vj5 For this version, the task was downsampled to 10 percent. Compute a new target Reported_Time. Compute new date features, ignore some features and encode as features as factor variables.",
    "format": "arff",
    "uploader": "Leo Grin",
    "uploader_id": 26324,
    "visibility": "public",
    "creator": null,
    "contributor": "\"Leo Grin\"",
    "date": "2023-01-11 17:45:37",
    "update_comment": null,
    "last_update": "2023-01-11 17:45:37",
    "licence": "CC0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22111941\/dataset",
    "default_target_attribute": "ReportedTime",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "seattlecrime6",
            "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 both numerical and categorical features\" benchmark. Original link: https:\/\/openml.org\/d\/42496 Original description: This data represents crime reported to the Seattle Police Department (SPD). Each row contains the record of a unique event where at least one criminal offense was reported by a member of the community or detected by an offi "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 52031,
        "NumberOfFeatures": 5,
        "NumberOfClasses": 0,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 3,
        "NumberOfSymbolicFeatures": 2,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 0,
        "PercentageOfMissingValues": 0,
        "AutoCorrelation": -478.0013838170286,
        "PercentageOfNumericFeatures": 60,
        "Dimensionality": 9.609655782129885e-5,
        "PercentageOfSymbolicFeatures": 40,
        "MajorityClassPercentage": null,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 0
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Geography"
        }
    ],
    "features": [
        {
            "name": "ReportedTime",
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            "type": "numeric",
            "distinct": "29",
            "missing": "0",
            "target": "1",
            "min": "0",
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            "name": "Precinct",
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            "distr": []
        },
        {
            "name": "Sector",
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        {
            "name": "Occurred_min",
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            "missing": "0",
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    "nr_of_issues": 0,
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