{
  "fetched_at": "2026-10-20T03:39:55Z",
  "source_url": "https://www.kaggle.com/competitions/csaisummerschool/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": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sample_submission.csv</strong> - a sample submission file in the correct format</li>\n<li><strong>skeleton_code.py</strong> - a skeleton python script to read train and test data and write output to the submission file.</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>Id</strong> - the id of the email</li>\n<li><strong>date</strong> - date and time at which mail was received</li>\n<li><strong>org</strong> - the organisation of the sender</li>\n<li><strong>tld</strong> - top-level domain of the sender's organisation</li>\n<li><strong>ccs</strong> - number of people cc'd in the email</li>\n<li><strong>bcced</strong> - is the receiver bcc'd in the email</li>\n<li><strong>mail_type</strong> - the type of the mail body</li>\n<li><strong>images</strong> - number of images in the mail body</li>\n<li><strong>urls</strong> - number of urls in the mail body</li>\n<li><strong>salutations</strong> - is salutation used in the email?</li>\n<li><strong>designation</strong> - is designation of the sender mentioned in the email?</li>\n<li><strong>chars_in_subject</strong> - number of characters in the email's subject</li>\n<li><strong>chars_in_body</strong> - number of characters in the email's body</li>\n<li><strong>label</strong> - the label of the email</li>\n</ul>\n",
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    {
      "name": "Description",
      "content": "<p>We often face the problem of searching meaningful emails from thousands of promotional emails. This challenge focuses on creating a binary classifier that can classify an email based on metadata extracted from the email.</p>\n<p>See data description to know more about the data.</p>",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/F1_score\">F1-Score</a>. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The 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.</p>\n<h2>Submission Format</h2>\n<p><strong>For every email in the dataset</strong>, submission files should contain two columns: Id and label.</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>Id,label<br />0,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">1,0<br />2,1<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">3,0<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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