{
  "fetched_at": "2026-10-19T11:33:55Z",
  "source_url": "https://www.kaggle.com/competitions/creditriskinbankingxgboost/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>Data file data_devsample.csv holds data to be used for development of the model. Binary variable describing, if the client repaid his or her obligations succesfully is in column TARGET.<\\p>\n\n<p>Data file data_to_score.csv contains data sample to be scored by the model.<\\p>",
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    {
      "name": "Description",
      "content": "<p>Financial institutions usually use statistical models to estimate credibility of applicants for a loan. Applicant with the higest risk are declined, those who are appoved can be assigned different available product parameters based on their score.</p>\n\n<p>Using provided data set, create an extreme gradient boosting model, that will serve for assessment of applicants creditbility. Quality of the model will be measured by its separation power - Gini coefficient.</p>\n\n<p>To pass the competition sucessfully, competitiors should reach Gini higher than 48.5 Gini (equivalent to AUC 0.7425) on the public leaderboard.</p>",
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    {
      "name": "Evaluation",
      "content": "<p>\nPerformance of the logistic regression model will be measure by its power to separate good and bad clients - Gini coefficient.\n</p>\n<h2>Submission Format</h2>\n<p>\nFor every out of sample application ID (SK_ID_CURR), submission files should contain two columns: SK_ID_CURR and estimated probability of default or its logit transformation (score).\n</p>\n<p>The file should contain a header, be in cvs file with comma as a separator. First column is named SK_ID_CURR and the second one SCORE.</p>\n<pre>SK_ID_CURR,SCORE<br>1111111,0.1<br></pre>",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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