{
  "fetched_at": "2026-10-24T06:35:25Z",
  "source_url": "https://www.kaggle.com/competitions/dsucsb-s19/data",
  "pages": [
    {
      "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": "data-description",
      "content": "<p>You are given two files: train.csv and test.csv. </p>\n\n<h2>Files</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set. Should be used to train your model</li>\n<li><strong>test.csv</strong> - the test set. Has had the clicks column removed. Use to generate your submission</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>jobId</strong> - an anonymous id unique to a given job posting</li>\n<li><strong>TBD</strong> - the rest will be filled in later</li>\n</ul>",
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    {
      "name": "Description",
      "content": "<p> Can we predict the number of clicks an online job posting will recieive?</p>\n<p>Using Indeed's dataset from Chapman Datafest 2018, we can try to predict how popular a given job posting will be. This will be a chance to apply your data science skills on messy, real world data in a low stakes competition.</p>\n\n<h2>Acknowledgements</h2>\n<p>We thank Indeed and Chapman University for providing this data</p>",
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    {
      "name": "Evaluation",
      "content": "\n<p>The evaluation metric for this competition is the <a href=\"https://en.wikipedia.org/wiki/Root-mean-square_deviation\"> RMSE (Root Mean Square Error)</a>. \n</p><hr>\n<p></p><h2>Submission Format</h2>\n<p><strong>For every job posting in the dataset</strong>, submission files should contain two columns: jobId and clicks. </p>\n<p>The file should contain a header and have the following format:</p>\n<pre>jobId,clicks<br>job0000001,4<br></pre>",
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