{
    "data_id": "46092",
    "name": "Loan_Approval_Dataset",
    "exact_name": "Loan_Approval_Dataset",
    "version": 1,
    "version_label": null,
    "description": "Description:\nThe loan_approval_dataset.json dataset is meticulously curated to aid in the assessment and prediction of loan approval processes. Comprising unique records identified by an Id, it encompasses a variety of features such as Income, Age, Experience, Marital Status, House Ownership, Car Ownership, Profession, City, State, Current Job Tenure, Current House Tenure, and Risk Flag, making it a comprehensive resource for financial analysis. The dataset's diversity in demographic and financial variables allows for a nuanced understanding of factors influencing loan approval decisions.\n\nAttribute Description:\n- Id: Unique identifier for each applicant.\n- Income: Annual income of the applicant in local currency.\n- Age: Applicant's age in years.\n- Experience: Number of years the applicant has work experience.\n- Married\/Single: Marital status of the applicant.\n- House_Ownership: The applicant's house ownership status, categories include 'rented', 'norent_noown', and 'owned'.\n- Car_Ownership: Indicates if the applicant owns a car ('yes' or 'no').\n- Profession: Applicant's current profession.\n- CITY: The city of residence of the applicant.\n- STATE: The state of residence of the applicant, with specific codes.\n- CURRENT_JOB_YRS: The number of years the applicant has been in their current job.\n- CURRENT_HOUSE_YRS: The number of years the applicant has lived in their current house.\n- Risk_Flag: Indicates the risk factor of the loan not being repaid (1) or being repaid (0).\n\nUse Case:\nThis dataset is ideal for financial institutions and machine learning practitioners to model and predict loan approval outcomes. By analyzing patterns and correlations within this dataset, users can develop predictive models to assess the likelihood of an applicant's ability to repay a loan, thus aiding in risk management and decision-making processes for loan issuance. Additionally, the dataset can be used for demographic studies, analyzing the impact of various factors like income, profession, and house ownership on loan approval chances.",
    "format": "arff",
    "uploader": "Iwo Godzwon",
    "uploader_id": 39999,
    "visibility": "public",
    "creator": "\"Rohit Sharma\"",
    "contributor": "\"None\"",
    "date": "2024-05-31 15:39:02",
    "update_comment": null,
    "last_update": "2024-05-31 15:39:02",
    "licence": "Attribution (CC BY)",
    "status": "in_preparation",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22120536\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "Loan_Approval_Dataset",
            "Description: The loan_approval_dataset.json dataset is meticulously curated to aid in the assessment and prediction of loan approval processes. Comprising unique records identified by an Id, it encompasses a variety of features such as Income, Age, Experience, Marital Status, House Ownership, Car Ownership, Profession, City, State, Current Job Tenure, Current House Tenure, and Risk Flag, making it a comprehensive resource for financial analysis. The dataset's diversity in demographic and financi "
        ],
        "weight": 5
    },
    "qualities": [],
    "tags": [],
    "features": [],
    "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,
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}