{
    "data_id": "43341",
    "name": "Diabetes-Dataset-2019",
    "exact_name": "Diabetes-Dataset-2019",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nThis dataset was collected by Neha Prerna Tigga and Dr. Shruti Garg of the Department of Computer Science and Engineering, BIT Mesra, Ranchi-835215 for research, non-commercial purposes only. An article is also published implementing this dataset. For more information and citation of this dataset please refer: \nTigga, N. P.,  Garg, S. (2020). Prediction of Type 2 Diabetes using Machine Learning Classification Methods. Procedia Computer Science, 167, 706-716. DOI: https:\/\/doi.org\/10.1016\/j.procs.2020.03.336\nContent\nThere is a total of 952 instances with 17 independent predictor variables and one binary target or dependent variable, Diabetes. \nAcknowledgements\nWe would like to thank all the participants who contributed towards the building of this dataset.\nInspiration\nTo build a machine learning algorithm to predict if a person has diabetes or not?",
    "format": "arff",
    "uploader": "Dustin Carrion",
    "uploader_id": 30123,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-23 12:18:45",
    "update_comment": null,
    "last_update": "2022-03-23 12:18:45",
    "licence": "Attribution 4.0 International (CC BY 4.0)",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102166\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
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    "runs": 0,
    "suggest": {
        "input": [
            "Diabetes-Dataset-2019",
            "Context This dataset was collected by Neha Prerna Tigga and Dr. Shruti Garg of the Department of Computer Science and Engineering, BIT Mesra, Ranchi-835215 for research, non-commercial purposes only. An article is also published implementing this dataset. For more information and citation of this dataset please refer: Tigga, N. P., Garg, S. (2020). Prediction of Type 2 Diabetes using Machine Learning Classification Methods. Procedia Computer Science, 167, 706-716. DOI: https:\/\/doi.org\/10.1016\/j. "
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    "qualities": {
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        "NumberOfFeatures": 18,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 48,
        "NumberOfInstancesWithMissingValues": 47,
        "NumberOfNumericFeatures": 4,
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        "PercentageOfInstancesWithMissingValues": 4.936974789915967,
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    "tags": [
        {
            "uploader": "38960",
            "tag": "Machine Learning"
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            "name": "Age",
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            "type": "string",
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