{
  "fetched_at": "2026-10-19T19:31:22Z",
  "source_url": "https://www.kaggle.com/competitions/dmassign1/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 contains two files that you are supposed to download and work on. They are as mentioned below:</p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>dataset.csv</strong> - the entire dataset</li>\n<li><strong>sample_submission.csv</strong> - a sample submission file in the correct format</li>\n</ul>",
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    {
      "name": "Description",
      "content": "<p>This is the home page of the competition. As the description of the competition suggests you are given a dataset with 199 attributes (including ID and Class) and 13000 instances and your task is to classify the given dataset into 5 classes [1,2,3,4,5] using only <strong>Clustering</strong> algorithms. </p>",
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    {
      "name": "Evaluation",
      "content": "<h2>Submission Format</h2>\n<p>You need to submit classes of 11700 tuples which are not labelled in the dataset. The submission file must contain exactly two attributes: 'ID' of the tuples and its corresponding 'Class' label.</p> \n<p>The submission file should contain a header and have the following format:</p>\n<pre>id,Class<br />id1300,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">id1301,1<br />id1302,1<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">id1303,1<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>\n<p> For your reference a sample_submission.csv has been uploaded in the Data tab that will clarify any doubt regarding submission format.  </p>\n<hr />\n<p>You are given 7 days with 5 submissions on each day.</p>\n<p>Your submissions will be evaluated on 50% of the dataset to give your results on the public\nleaderboard. At the end of the competition, your selected submissions (you can select\nmaximum 2 submissions) will be evaluated on rest 50% of the data and will be shown on the\nprivate leaderboard. However, the final score will be based on your performance on 100%\ndata.</p>\n<p>The evaluation metric for this competition is <strong>Accuracy</strong>. The <strong>Accuracy</strong> score is given by:  \\[\\frac{\\#CorrectPredictions}{Total Labels(11700)}\\] </p>\n<p><h2>Note:</h2> <strong>AFTER<strong> the competition ends, you need to upload the jupyter notebook that gave you your best result (corresponding to the public leaderboard) as a kernel on Kaggle. The result obtained on running your uploaded kernel must match your score on the private leaderboard. In case of mismatch, no marks will be awarded.</p>",
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