{
  "fetched_at": "2026-10-19T23:34:53Z",
  "source_url": "https://www.kaggle.com/competitions/cesma2019/data",
  "pages": [
    {
      "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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    {
      "name": "data-description",
      "content": "<p> This exercise presents a complete data set (with the target) and a test dataset without targets. You must estimate the target in the test dataset, then write a file that contains the ID and probability of the TARGET (the probability that the target = 1). </p>\n<p> The file obtained must be submitted to KAGGLE for evaluation, you will see your score on the public Leaderboard. </p>\n\n<p> I put a program kernel that reads and writes data and suggests some suggestions. The program must be completed, but you can use it to avoid getting bogged down in reading and writing data. </p>\n<p> Once satisfied with your work, you must send me an email with 2 pages of report on the work done, also commenting on the result obtained </p>\n<ul>\n<li><strong>train1.csv</strong> - the training set</li>\n<li><strong>test1.csv</strong> - the test set</li>\n<li><strong>es_soluz.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n",
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    },
    {
      "name": "Description",
      "content": "<p></p><h2>This is the home page of the competition. </h2><p></p>\nIn this competition you must analyze bank data from current accounts and identify insolvent individuals. You have 60,000 training units available to estimate the model, which you must then use to estimate the probability of default in 16020 units, of which you do not have the target.",
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    },
    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is  the AUC-Score. </p>\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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}