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  "fetched_at": "2026-10-19T21:42:48Z",
  "source_url": "https://www.kaggle.com/competitions/cs6271-kagglecompetition/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Files\n\n*   **USA_census_train.csv** - the training set\n*   **USA_census_test.csv** - the test set\n*   **USA_census_submission.csv** - a sample submission file in the correct format",
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      "name": "Evaluation",
      "content": "## Evaluation\n\n[Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification) is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. \n- Accuracy is the proportion of correct predictions, both true positives and true negatives among the total number of cases examined.\n- It compares estimates of pre- and post-test probability. \n- It is a parameter of the test. \n- The formula for quantifying binary accuracy is:\n\n\\[ Accuracy = \\frac{TP + TN}{TP + TN + FP + FN}  \\]\n\nwhere TP = True positive; FP = False positive; TN = True negative; FN = False negative\n\n## Submission\n\n**For every author in the dataset**, submission files should contain two columns: `id` and `class_label`. The file should contain a header and have the following format:\n\n```\nid, class_label\n1,   0\n2,   1\n3,   1\n4,   0\n5,   1\n.    .\n.    .\n.    .\n```",
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    {
      "name": "rules",
      "content": "- This competition was open (**closed now**) only to students of CS6271 from the University of Limerick;\n- You may enter as an individual or as a team; in each case please choose a team name;\n- You may enter up to five times per day. Please see the **CS6271 Sulis** site for full details;\n- A sample notebook is available in the CS6271 GitHub repo -- you can, but don't have to use that, however, it does have the functionality to generate a test file to upload.",
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    {
      "name": "Note",
      "content": "This competition is already closed.",
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