{
    "data_id": "43157",
    "name": "insurance_dataset",
    "exact_name": "insurance_dataset",
    "version": 1,
    "version_label": null,
    "description": "**Dataset description**\n\nInsurance is a network for evaluating car insurance risks.\n\n\n**Format of the dataset**\n\n\nThe insurance data set contains the following 27 variables:\n\nGoodStudent (good student): a two-level factor with levels False and True.\n\nAge (age): a three-level factor with levels Adolescent, Adult and Senior.\n\nSocioEcon (socio-economic status): a four-level factor with levels Prole, Middle, UpperMiddle and Wealthy.\n\nRiskAversion (risk aversion): a four-level factor with levels Psychopath, Adventurous, Normal and Cautious.\n\nVehicleYear (vehicle age): a two-level factor with levels Current and older.\n\nThisCarDam (damage to this car): a four-level factor with levels None, Mild, Moderate and Severe.\n\nRuggedAuto (ruggedness of the car): a three-level factor with levels EggShell, Football and Tank.\n\nAccident (severity of the accident): a four-level factor with levels None, Mild, Moderate and Severe.\n\nMakeModel (car's model): a five-level factor with levels SportsCar, Economy, FamilySedan, Luxury and SuperLuxury.\n\nDrivQuality (driving quality): a three-level factor with levels Poor, Normal and Excellent.\n\nMileage (mileage): a four-level factor with levels FiveThou, TwentyThou, FiftyThou and Domino.\n\nAntilock (ABS): a two-level factor with levels False and True.\n\nDrivingSkill (driving skill): a three-level factor with levels SubStandard, Normal and Expert.\n\nSeniorTrain (senior training): a two-level factor with levels False and True.\n\nThisCarCost (costs for the insured car): a four-level factor with levels Thousand, TenThou, HundredThou and Million.\n\nTheft (theft): a two-level factor with levels False and True.\n\nCarValue (value of the car): a five-level factor with levels FiveThou, TenThou, TwentyThou, FiftyThou and Million.\n\nHomeBase (neighbourhood type): a four-level factor with levels Secure, City, Suburb and Rural.\n\nAntiTheft (anti-theft system): a two-level factor with levels False and True.\n\nPropCost (ratio of the cost for the two cars): a four-level factor with levels Thousand, TenThou, HundredThou and Million.\n\nOtherCarCost (costs for the other car): a four-level factor with levels Thousand, TenThou, HundredThou and Million.\n\nOtherCar (other cars involved in the accident): a two-level factor with levels False and True.\n\nMedCost (cost of the medical treatment): a four-level factor with levels Thousand, TenThou, HundredThou and Million.\n\nCushioning (cushioning): a four-level factor with levels Poor, Fair, Good and Excellent.\n\nAirbag (airbag): a two-level factor with levels False and True.\n\nILiCost (inspection cost): a four-level factor with levels Thousand, TenThou, HundredThou and Million.\n\nDrivHist (driving history): a three-level factor with levels Zero, One and Many.\n\n**Source **\n\nBinder J, Koller D, Russell S, Kanazawa K (1997). \"Adaptive Probabilistic Networks with Hidden Variables\". Machine Learning, 29(2-3):213-244.",
    "format": "arff",
    "uploader": "Oleksandr Zadorozhnyi",
    "uploader_id": 29044,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-01-31 11:55:05",
    "update_comment": null,
    "last_update": "2022-01-31 11:55:05",
    "licence": "Public",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22101752\/data.arff",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "insurance_dataset",
            "Insurance is a network for evaluating car insurance risks. The insurance data set contains the following 27 variables: GoodStudent (good student): a two-level factor with levels False and True. Age (age): a three-level factor with levels Adolescent, Adult and Senior. SocioEcon (socio-economic status): a four-level factor with levels Prole, Middle, UpperMiddle and Wealthy. RiskAversion (risk aversion): a four-level factor with levels Psychopath, Adventurous, Normal and Cautious. VehicleYear (vehi "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 20000,
        "NumberOfFeatures": 27,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 0,
        "NumberOfSymbolicFeatures": 27,
        "Dimensionality": 0.00135,
        "PercentageOfNumericFeatures": 0,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 100,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 8,
        "PercentageOfBinaryFeatures": 29.629629629629626,
        "PercentageOfInstancesWithMissingValues": 0,
        "AutoCorrelation": null,
        "PercentageOfMissingValues": 0
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Machine Learning"
        },
        {
            "uploader": "38960",
            "tag": "Mathematics"
        }
    ],
    "features": [
        {
            "name": "GoodStudent",
            "index": "0",
            "type": "nominal",
            "distinct": "2",
            "missing": "0",
            "distr": []
        },
        {
            "name": "Age",
            "index": "1",
            "type": "nominal",
            "distinct": "3",
            "missing": "0",
            "distr": []
        },
        {
            "name": "SocioEcon",
            "index": "2",
            "type": "nominal",
            "distinct": "4",
            "missing": "0",
            "distr": []
        },
        {
            "name": "RiskAversion",
            "index": "3",
            "type": "nominal",
            "distinct": "4",
            "missing": "0",
            "distr": []
        },
        {
            "name": "VehicleYear",
            "index": "4",
            "type": "nominal",
            "distinct": "2",
            "missing": "0",
            "distr": []
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        {
            "name": "ThisCarDam",
            "index": "5",
            "type": "nominal",
            "distinct": "4",
            "missing": "0",
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            "name": "RuggedAuto",
            "index": "6",
            "type": "nominal",
            "distinct": "3",
            "missing": "0",
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        {
            "name": "Accident",
            "index": "7",
            "type": "nominal",
            "distinct": "4",
            "missing": "0",
            "distr": []
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        {
            "name": "MakeModel",
            "index": "8",
            "type": "nominal",
            "distinct": "5",
            "missing": "0",
            "distr": []
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        {
            "name": "DrivQuality",
            "index": "9",
            "type": "nominal",
            "distinct": "3",
            "missing": "0",
            "distr": []
        },
        {
            "name": "Mileage",
            "index": "10",
            "type": "nominal",
            "distinct": "4",
            "missing": "0",
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            "name": "Antilock",
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            "type": "nominal",
            "distinct": "2",
            "missing": "0",
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            "name": "DrivingSkill",
            "index": "12",
            "type": "nominal",
            "distinct": "3",
            "missing": "0",
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        {
            "name": "SeniorTrain",
            "index": "13",
            "type": "nominal",
            "distinct": "2",
            "missing": "0",
            "distr": []
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        {
            "name": "ThisCarCost",
            "index": "14",
            "type": "nominal",
            "distinct": "4",
            "missing": "0",
            "distr": []
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        {
            "name": "Theft",
            "index": "15",
            "type": "nominal",
            "distinct": "2",
            "missing": "0",
            "distr": []
        },
        {
            "name": "CarValue",
            "index": "16",
            "type": "nominal",
            "distinct": "5",
            "missing": "0",
            "distr": []
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        {
            "name": "HomeBase",
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            "type": "nominal",
            "distinct": "4",
            "missing": "0",
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        {
            "name": "AntiTheft",
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            "type": "nominal",
            "distinct": "2",
            "missing": "0",
            "distr": []
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            "name": "PropCost",
            "index": "19",
            "type": "nominal",
            "distinct": "4",
            "missing": "0",
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        },
        {
            "name": "OtherCarCost",
            "index": "20",
            "type": "nominal",
            "distinct": "3",
            "missing": "0",
            "distr": []
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        {
            "name": "OtherCar",
            "index": "21",
            "type": "nominal",
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            "missing": "0",
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            "name": "MedCost",
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            "type": "nominal",
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            "missing": "0",
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        {
            "name": "Cushioning",
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            "type": "nominal",
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            "missing": "0",
            "distr": []
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        {
            "name": "Airbag",
            "index": "24",
            "type": "nominal",
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            "name": "ILiCost",
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            "type": "nominal",
            "distinct": "4",
            "missing": "0",
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        {
            "name": "DrivHist",
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            "type": "nominal",
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            "missing": "0",
            "distr": []
        }
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