{
    "data_id": "43754",
    "name": "German-Credit-Risk",
    "exact_name": "German-Credit-Risk",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nThe original dataset contains 1000 entries with 20 categorial\/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below.\nContent\nIt is almost impossible to understand the original dataset due to its complicated system of categories and symbols. Thus, I wrote a small Python script to convert it into a readable CSV file. Several columns are simply ignored, because in my opinion either they are not important or their descriptions are obscure. The selected attributes are:\n\nAge (numeric)\nSex (text: male, female)\nJob (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled)\nHousing (text: own, rent, or free)\nSaving accounts (text - little, moderate, quite rich, rich)\nChecking account (numeric, in DM - Deutsch Mark)\nCredit amount (numeric, in DM)\nDuration (numeric, in month)\nPurpose (text: car, furniture\/equipment, radio\/TV, domestic appliances, repairs, education, business, vacation\/others)\n\nAcknowledgements\nSource: UCI",
    "format": "arff",
    "uploader": "Dustin Carrion",
    "uploader_id": 30123,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-24 07:51:48",
    "update_comment": null,
    "last_update": "2022-03-24 07:51:48",
    "licence": "Database: Open Database, Contents: Database Contents",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102579\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "German-Credit-Risk",
            "Context The original dataset contains 1000 entries with 20 categorial\/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below. Content It is almost impossible to understand the original dataset due to its complicated system of categories and symbols. Thus, I wrote a small Python script to "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 1000,
        "NumberOfFeatures": 10,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 577,
        "NumberOfInstancesWithMissingValues": 478,
        "NumberOfNumericFeatures": 5,
        "NumberOfSymbolicFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 47.8,
        "AutoCorrelation": null,
        "PercentageOfMissingValues": 5.7700000000000005,
        "Dimensionality": 0.01,
        "PercentageOfNumericFeatures": 50,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 0
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Government"
        }
    ],
    "features": [
        {
            "name": "Unnamed:_0",
            "index": "0",
            "type": "numeric",
            "distinct": "1000",
            "missing": "0",
            "min": "0",
            "max": "999",
            "mean": "500",
            "stdev": "289"
        },
        {
            "name": "Age",
            "index": "1",
            "type": "numeric",
            "distinct": "53",
            "missing": "0",
            "min": "19",
            "max": "75",
            "mean": "36",
            "stdev": "11"
        },
        {
            "name": "Sex",
            "index": "2",
            "type": "string",
            "distinct": "2",
            "missing": "0"
        },
        {
            "name": "Job",
            "index": "3",
            "type": "numeric",
            "distinct": "4",
            "missing": "0",
            "min": "0",
            "max": "3",
            "mean": "2",
            "stdev": "1"
        },
        {
            "name": "Housing",
            "index": "4",
            "type": "string",
            "distinct": "3",
            "missing": "0"
        },
        {
            "name": "Saving_accounts",
            "index": "5",
            "type": "string",
            "distinct": "4",
            "missing": "183"
        },
        {
            "name": "Checking_account",
            "index": "6",
            "type": "string",
            "distinct": "3",
            "missing": "394"
        },
        {
            "name": "Credit_amount",
            "index": "7",
            "type": "numeric",
            "distinct": "921",
            "missing": "0",
            "min": "250",
            "max": "18424",
            "mean": "3271",
            "stdev": "2823"
        },
        {
            "name": "Duration",
            "index": "8",
            "type": "numeric",
            "distinct": "33",
            "missing": "0",
            "min": "4",
            "max": "72",
            "mean": "21",
            "stdev": "12"
        },
        {
            "name": "Purpose",
            "index": "9",
            "type": "string",
            "distinct": "8",
            "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
}