{
  "fetched_at": "2026-10-20T13:58:42Z",
  "source_url": "https://www.kaggle.com/competitions/data-mining-associative-rules/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\n\n## 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*   **metaData.csv** - supplemental information about the data\n\n## Columns\n\n*   `example_id` - definition of example_id\n*   `feature_1` - definition of feature_1\n*   etc.",
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    {
      "name": "Description",
      "content": "\nHere is a handy link to Kaggle's [competition documentation](https://www.kaggle.com/docs/competitions), which includes, among other things, instructions on [submitting predictions](https://www.kaggle.com/docs/competitions#making-a-submission).\n\n\n## This is a subtitle\nTo format pages, stick to the following conventions:\n- Inline `code` should be encapsulated by back-ticks \\``. \n- Block code should have  \\``` as the first and last line of the block to produce\n```\n# code to print hello\nprint('hello')\n```\n- Subtitles are prefixed with \\##\n\n\nYou can display block equations using LaTeX encapsulated between `\\[` and `\\]`, and display inline encapsulated between `\\(` and `\\)` . Note that underscores should be escaped with a `\\` for the LaTeX to render properly. For example, this:\n\n\\[ \\epsilon = \\sqrt{\\frac{1}{n} \\sum\\_{i=1}^n (\\log(p\\_i + 1) - \\log(a\\_i+1))^2 } \\]\n\nis created by this:\n\n`\\[\\epsilon = \\sqrt{\\frac{1}{n} \\sum\\_{i=1}^n (\\log(p\\_i + 1) - \\log(a\\_i+1))^2 }\\]`\n\n\n## Acknowledgements\n\nWe thank Professor Plum, Ph.D. for providing this dataset.",
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    {
      "name": "Evaluation",
      "content": "The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top; the page title appears above. For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have included below:\ns \\(\\text{tp} + \\text{fn}\\). The F1 score is given by:\n\n\\[ \\text{F1} = 2\\frac{\\text{p} \\cdot \\text{r}}{\\text{p}+\\text{r}}\\ \\ \\mathrm{where}\\ \\ \\text{p} = \\frac{\\text{tp}}{\\text{tp}+\\text{fp}},\\ \\ \\text{r} = \\frac{\\text{tp}}{\\text{tp}+\\text{fn}} \\]\n\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.\n\n## Submission Format\n\n**For every author in the dataset**, submission files should contain two columns: `AuthorId` and `DuplicateAuthorIds`. `DuplicateAuthorIds` should be a space-delimited list. Every `AuthorId` counts as its own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list `A,A B C`, `B,B A C`, `C,C A B`.\n\n\nThe file should contain a header and have the following format:\n```\nAuthorId,DuplicateAuthorIds\n1,1\n```",
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    {
      "name": "rules",
      "content": "\n - Have fun!",
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