{
  "fetched_at": "2026-10-19T10:44:01Z",
  "source_url": "https://www.kaggle.com/competitions/ai511-homeloan-2022/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Files\n\n*   **train_data.csv** - the training set\n*   **test_data.csv** - the test set\n*   **sample_solution.csv** - a sample submission file in the correct format\n*   **columns_description.csv** - supplemental information about the data; read this to know more about the columns in `train_data.csv`",
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    {
      "name": "Description",
      "content": "Many people struggle to get loans due to **insufficient** or **non-existent credit histories.** And, unfortunately, this population is often taken advantage of by untrustworthy lenders.\n\nHome Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities.",
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    {
      "name": "Evaluation",
      "content": "Submissions are evaluated on TARGET column using the **Macro F Score**\n\nFor each `SK_ID_CURR` in the test set, you must predict the `TARGET` i.e predict if the customer with that ID will default their loan or not. ",
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    {
      "name": "rules",
      "content": "**AI511 Course rules apply.**",
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