{
  "fetched_at": "2026-10-20T02:53:25Z",
  "source_url": "https://www.kaggle.com/competitions/credit-card-default/data",
  "pages": [
    {
      "name": "rules",
      "content": "\n\t<!-- Begin Base Rules -->\n    <h3>One account per participant</h3>\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n    <h3>No private sharing outside teams</h3>\n    <p>\n        Privately sharing code or data outside of teams is not permitted.\n        It's okay to share code if made available to all participants on the forums.\n    </p>\n    <h3>Team Mergers</h3>\n    <p>Team mergers are not allowed in this competition.</p>\n\n    <h3>Team Limits</h3>\n    <p>There is no maximum team size.</p>\n    <h3>Submission Limits</h3>\n    <p>You may submit a maximum of 20 entries per day.</p>\n    <p>You may select up to 2 final submissions for judging.</p>\n    <h3>Competition Timeline</h3>\n    <p>Start Date: <strong>TBA</strong></p>\n    <p>Merger Deadline: <strong>None</strong></p>\n    <p>Entry Deadline: <strong>None</strong></p>\n    <p>End Date: <strong>12/9/2018 12:00 AM UTC</strong></p>\n\t<!-- End Base Rules -->\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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      "name": "data-description",
      "content": "\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n<li><strong>metaData.csv</strong> - supplemental information about the data</li>\n</ul>",
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    {
      "name": "Credit Card Defaulters Identification",
      "content": "<p>Credit cards are part-convenience, part-curse for those who don’t use them wisely. \nPulling out the plastic to pay for things you can’t really afford and only paying the \nminimum each month is the easiest way to land yourself in debt. When the bills grow to \nthe point where you can’t pay, you may be tempted to just turn your back on them altogether.</p>\n\n",
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    {
      "name": "Evaluation",
      "content": "<p>The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) (or sometimes root-mean-squared error) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. The RMSD represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences.</p>\n<hr />\n<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/Root-mean-square_deviation\"> RMSE-Score</a>. ",
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    {
      "name": "Description",
      "content": "<p>This research aimed at the case of customers default payments in Taiwan and compares the predictive accuracy of probability of default among \nsix data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default \nwill be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default \nis unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default. With the real probability \nof default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression \nresult (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; \nits regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, \nartificial neural network is the only one that can accurately estimate the real probability of default.</p>",
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