{
    "data_id": "43439",
    "name": "Medical-Appointment-No-Shows",
    "exact_name": "Medical-Appointment-No-Shows",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nA person makes a doctor appointment, receives all the instructions and no-show. Who to blame? \nIf this help you studying or working, please dont forget to upvote :).  Reference to Joni Hoppen and Aquarela Advanced Analytics  Aquarela\nGreetings! \nContent\n110.527 medical appointments its 14 associated variables (characteristics). The most important one if the patient show-up or no-show to the appointment.  Variable names are self-explanatory, if you have doubts, just let me know! \nscholarship variable means this concept = https:\/\/en.wikipedia.org\/wiki\/Bolsa_FamC3ADlia\n14 variables \nData Dictionary\n01 - PatientId\n\nIdentification of a patient\n\n02 - AppointmentID\n\nIdentification of each appointment\n\n03 - Gender\n\nMale or Female . Female is the greater proportion, woman takes way more care of they health in comparison to man.\n\n04 - DataMarcacaoConsulta\n\nThe day of the actuall appointment, when they have to visit the doctor.\n\n05 - DataAgendamento\n\nThe day someone called or registered the appointment, this is before appointment of course.\n\n06 - Age\n\nHow old is the patient.\n\n07 - Neighbourhood\n\nWhere the appointment takes place. \n\n08 - Scholarship\n\nTrue of False . Observation, this is a broad topic, consider reading this article https:\/\/en.wikipedia.org\/wiki\/Bolsa_FamC3ADlia \n\n09 - Hipertension\n\nTrue or False\n\n10 - Diabetes\n\nTrue or False\n\nAlcoholism\n\nTrue or False\n\nHandcap\n\nTrue or False\n\nSMS_received\n\n1 or more messages sent to the patient.\n\nNo-show\n\nTrue or False. \n\nInspiration\nWhat if that possible to predict someone to no-show an appointment?",
    "format": "arff",
    "uploader": "Dustin Carrion",
    "uploader_id": 30123,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-23 13:21:02",
    "update_comment": null,
    "last_update": "2022-03-23 13:21:02",
    "licence": "CC BY-NC-SA 4.0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102264\/dataset",
    "default_target_attribute": "No-show",
    "row_id_attribute": null,
    "ignore_attribute": "\"PatientId\"",
    "runs": 0,
    "suggest": {
        "input": [
            "Medical-Appointment-No-Shows",
            "Context A person makes a doctor appointment, receives all the instructions and no-show. Who to blame? If this help you studying or working, please dont forget to upvote :). Reference to Joni Hoppen and Aquarela Advanced Analytics Aquarela Greetings! Content 110.527 medical appointments its 14 associated variables (characteristics). The most important one if the patient show-up or no-show to the appointment. Variable names are self-explanatory, if you have doubts, just let me know! scholarship va "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 110527,
        "NumberOfFeatures": 13,
        "NumberOfClasses": 2,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 8,
        "NumberOfSymbolicFeatures": 0,
        "Dimensionality": 0.00011761831950564115,
        "PercentageOfNumericFeatures": 61.53846153846154,
        "MajorityClassPercentage": 79.8067440534892,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassSize": 88208,
        "MinorityClassPercentage": 20.193255946510806,
        "MinorityClassSize": 22319,
        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 0,
        "AutoCorrelation": 1,
        "PercentageOfMissingValues": 0
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Computer Systems"
        },
        {
            "uploader": "38960",
            "tag": "Machine Learning"
        }
    ],
    "features": [
        {
            "name": "No-show",
            "index": "13",
            "type": "string",
            "distinct": "2",
            "missing": "0",
            "target": "1"
        },
        {
            "name": "PatientId",
            "index": "0",
            "type": "numeric",
            "distinct": "62299",
            "missing": "0",
            "ignore": "1",
            "min": "39218",
            "max": "2147483647",
            "mean": "2147483647",
            "stdev": "2147483647"
        },
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            "name": "AppointmentID",
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            "type": "numeric",
            "distinct": "110527",
            "missing": "0",
            "min": "5030230",
            "max": "5790484",
            "mean": "5675305",
            "stdev": "71296"
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            "name": "Gender",
            "index": "2",
            "type": "string",
            "distinct": "2",
            "missing": "0"
        },
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            "name": "ScheduledDay",
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            "distinct": "103549",
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            "name": "AppointmentDay",
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            "name": "Age",
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            "index": "6",
            "type": "string",
            "distinct": "81",
            "missing": "0"
        },
        {
            "name": "Scholarship",
            "index": "7",
            "type": "numeric",
            "distinct": "2",
            "missing": "0",
            "min": "0",
            "max": "1",
            "mean": "0",
            "stdev": "0"
        },
        {
            "name": "Hipertension",
            "index": "8",
            "type": "numeric",
            "distinct": "2",
            "missing": "0",
            "min": "0",
            "max": "1",
            "mean": "0",
            "stdev": "0"
        },
        {
            "name": "Diabetes",
            "index": "9",
            "type": "numeric",
            "distinct": "2",
            "missing": "0",
            "min": "0",
            "max": "1",
            "mean": "0",
            "stdev": "0"
        },
        {
            "name": "Alcoholism",
            "index": "10",
            "type": "numeric",
            "distinct": "2",
            "missing": "0",
            "min": "0",
            "max": "1",
            "mean": "0",
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        },
        {
            "name": "Handcap",
            "index": "11",
            "type": "numeric",
            "distinct": "5",
            "missing": "0",
            "min": "0",
            "max": "4",
            "mean": "0",
            "stdev": "0"
        },
        {
            "name": "SMS_received",
            "index": "12",
            "type": "numeric",
            "distinct": "2",
            "missing": "0",
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