{
  "fetched_at": "2026-10-24T17:58:48Z",
  "source_url": "https://www.kaggle.com/competitions/codeop-default-risk-v2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This competition has two main data files: - train.csv: this file contains all the data where the target is present. - test.csv: this file contains only the feature values that you will need to generate predictions and create a submission file. I've also added two auxiliary files: - sample_submission.csv: this file contains an example of how a submission should look like for Kaggle to take it. - HomeCredit_columns_description.csv: this file contains information on the columns of the train and test csv files.",
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      "postTitle": "",
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    },
    {
      "name": "Description",
      "content": "This kaggle-in-class is a simpler replica of [this](https://www.kaggle.com/c/home-credit-default-risk) other competition. It only uses one of the sources of data, the one present on the 'application' csv file. The data contains a bunch of information from conceded loans. The target 'default' is a binary variable that takes value 1 if the person was not able to repay the loan and 0 otherwise. For more detailed information, you can check their website.\n\nThis particular competition will be evaluated computing the [accuracy score](https://developers.google.com/machine-learning/crash-course/classification/accuracy) \n\nThe objective of this competition is to get familiar with kaggle competitions, the end-to-end flow of machine learning projects and apply the content we see in class while we see its impact on a real dataset.\n\nUp until now we've already seen the basics of supervised learning and classification problems, so you should be ready to already apply those.\n\nHave fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "This competition will be evaluated computing the AUC score - same as the original one. You can compute it using sklearn's function:\nhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html\n\nYou can take a look at the samplesubmission.csv file to check how the submission should look like. It should have a column with SKID_CURR (ID) and default (target).",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "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!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}