{
  "fetched_at": "2026-10-19T11:33:38Z",
  "source_url": "https://www.kaggle.com/competitions/creditriskscoringwithlogreg2022/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 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 a logistic regression 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 49 Gini on the undisclosed sample.",
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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---\n\n## Submission Format\n\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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