{
  "fetched_at": "2026-10-19T22:57:02Z",
  "source_url": "https://www.kaggle.com/competitions/2el1730-machinelearning/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>This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, 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.</p>\r\n<p>Participants should be able to answer questions like these after reading the data description: What files do I need? What should I expect the data format to be? What am I predicting? What acroynms will I encounter?</p>\r\n<h2>File descriptions</h2>\r\n<ul>\r\n<li><strong>train.csv</strong> - the training set</li>\r\n<li><strong>test.csv</strong> - the test set</li>\r\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\r\n<li><strong>metaData.csv</strong> - supplemental information about the data</li>\r\n<li>etc...</li>\r\n</ul>\r\n<h2>Data fields</h2>\r\n<ul>\r\n<li><strong>customer_id</strong> - an anonymous id unique to a given customer</li>\r\n<li><strong>movie_id</strong> - the id of a movie</li>\r\n<li><strong>movie_title</strong> - the title of a movie</li>\r\n<li>etc...</li>\r\n</ul>",
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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 multi-class classifier that can classify an email into four classes based on the 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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