{
    "data_id": "43390",
    "name": "Churn-for-Bank-Customers",
    "exact_name": "Churn-for-Bank-Customers",
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    "description": "Content\nChurn for bank customers\nRowNumbercorresponds to the record (row) number and has no effect on the output. \nCustomerIdcontains random values and has no effect on customer leaving the bank. \nSurnamethe surname of a customer has no impact on their decision to leave the bank. \nCreditScorecan have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank.\nGeographya customers location can affect their decision to leave the bank. \nGenderits interesting to explore whether gender plays a role in a customer leaving the bank. \nAgethis is certainly relevant, since older customers are less likely to leave their bank than younger ones.\nTenurerefers to the number of years that the customer has been a client of the bank. Normally, older clients are more loyal and less likely to leave a bank.\nBalancealso a very good indicator of customer churn, as people with a higher balance in their accounts are less likely to leave the bank compared to those with lower balances.\nNumOfProductsrefers to the number of products that a customer has purchased through the bank.\nHasCrCarddenotes whether or not a customer has a credit card. This column is also relevant, since people with a credit card are less likely to leave the bank.\nIsActiveMemberactive customers are less likely to leave the bank.\nEstimatedSalaryas with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.\nExitedwhether or not the customer left the bank.\n\nAcknowledgements\nAs we know, it is much more expensive to sign in a new client than keeping an existing one.\nIt is advantageous for banks to know what leads a client towards the decision to leave the company.\nChurn prevention allows companies to develop loyalty programs and retention campaigns to keep as many customers as possible.",
    "format": "arff",
    "uploader": "Onur Yildirim",
    "uploader_id": 30126,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-23 12:52:51",
    "update_comment": null,
    "last_update": "2022-03-23 12:52:51",
    "licence": "CC0: Public Domain",
    "status": "active",
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    "url": "https:\/\/www.openml.org\/data\/download\/22102215\/dataset",
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            "Churn-for-Bank-Customers",
            "Content Churn for bank customers RowNumbercorresponds to the record (row) number and has no effect on the output. CustomerIdcontains random values and has no effect on customer leaving the bank. Surnamethe surname of a customer has no impact on their decision to leave the bank. CreditScorecan have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank. Geographya customers location can affect their decision to leave the bank. Genderits interesti "
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        "Dimensionality": 0.0014,
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    "tags": [
        {
            "uploader": "38960",
            "tag": "Computer Systems"
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            "uploader": "38960",
            "tag": "Machine Learning"
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