{
  "fetched_at": "2026-10-20T09:37:36Z",
  "source_url": "https://www.kaggle.com/competitions/estu-stat-ml-comp-2025/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns of **sample_submission.csv**\n\n*   `ID` \n*   `class` \n",
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    {
      "name": "Description",
      "content": "Follow the instructions, dataset, and variables before working on the competition!\n\n## Instructions\n- This competition aims to train a superior classification model on test data.\n- You must use one of these models: logistic regression, decision tree, bagging tree, random forest, gradient boosting machines, extreme gradient boosting machines, or their ensembles.\n- You can transform, scale, or select the variables.\n- You are not limited to the directions above.\n- You can submit 5 solutions in a day.\n\n## Dataset \nThe dataset contains 1000 observations with 21 variables. In this dataset, each observation represents a person who takes credit from a bank. The target is that each person is classified as default or non-default according to the set of variables. \n\nThe target is Class variable (0: default, 1: non-default)",
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    {
      "name": "Evaluation",
      "content": "Submissions are evaluated on balanced accuracy between the predicted class and the observed class.\n\n## Submission File\nFor each ID (from 1 to 100) in the test set, you must predict a class for the Class variable. The .csv file should contain a header and have the following format:\n\n    ID, Class\n    1, 0\n    2, 1\n    3, 0\n    etc.\n\nPlease use your FULL NAME in submission!",
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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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    {
      "name": "abstract",
      "content": "**Goal:** This competition aims to train a superior classification model for a credit risk problem.",
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