{
  "fetched_at": "2026-10-22T06:53:35Z",
  "source_url": "https://www.kaggle.com/competitions/asdfasdfasdfasdfasdfasdfasdf/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This page appears alongside the data files. Itasdfasdfasdfasdf describes what files have been provided and the format of each. There is no single format for this page that is appropriate for all competitions, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.\n\nParticipants should be able to answer these types of questions after reading the data description:\n\nWhat files do I need?  \nWhat should I expect the data format to be?  \nWhat am I predicting?  \nWhat acronyms will I encounter?\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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      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "This iasdfasdfasdfasdfs the home page of the competition. You don't need a subtitle here. The competition sub-title will appear above. This is where you introduce the problem. You can upload images using the \"Insert Photo\" icon on the bar that displays above this textbox when in edit mode.\n\nIf you are copy-pasting from another application, like Word or your browser, you may need to clean up the markdown or html for things to display properly.\n\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.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "Evaluation",
      "content": "The evaluation pages describes how submissions will be scored and how students should format their sasdffdsubmissions. 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:\n\n---\n\nThe evaluation metric for this competition is [Mean F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision \\( \\text{p} \\) and recall \\( \\text{r} \\).\n\nPrecision is the ratio of true positives \\(\\text{tp}\\) to all predicted positives \\(\\text{tp} + \\text{fp}\\). Recall is the ratio of true positives \\(\\text{tp}\\) to all actual positives \\(\\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```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
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    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. Youasdfasdfasdf 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": "",
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  ]
}