{
  "fetched_at": "2026-10-19T11:33:25Z",
  "source_url": "https://www.kaggle.com/competitions/credit-risk-scoring-with-xgboost2021/data",
  "pages": [
    {
      "name": "data-description",
      "content": "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 named TARGET.\n\nData file data_to_score.csv contains data sample to be scored by the model.",
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    {
      "name": "Description",
      "content": "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.\n\nUsing 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.\n\nTo pass the competition sucessfully, competitiors should reach Gini higher than 52 Gini (equivalent to AUC 0.76) on the public leaderboard.",
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    {
      "name": "Evaluation",
      "content": "Performance of the logistic regression model will be measure by its power to separate good and bad clients - Gini coefficient.\n\n## Submission Format\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\nThe 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.\n\n```\nSK_ID_CURR,SCORE\n1111111,0.1\n```",
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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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