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  "fetched_at": "2026-10-19T23:13:08Z",
  "source_url": "https://www.kaggle.com/competitions/assignment3-multi-class-classification-ecs308-658/data",
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    {
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      "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</ul>\n\n\n<h2>Data fields</h2>\n<ul>\n<li><strong>id</strong> - array index </li>\n<li><strong>class</strong> - single digit (actual result will be announced later)</li>\n</ul>\n\n<h2>Submission format</h2>\nid,class <br>\n0,5 <br>\n1,9 <br>\n2,2 <br>\n3,8 <br>\n. <br>\n. <br>\n1999,1 <br>",
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      "content": "<p> In this assignment, you will be trying to model and learn an SVM classifier for images, using just the given features. </p> \n\n<p> We provide an extended version of the Assignment 3 image dataset. This dataset has 10,000 instances of a similar set of 25features (compared to the 3,000 instances in Part 1 of Assignment 3), and you are required to train a multiclass SVM model for predicting the labels on a target set of 2,000 instances. </p>\n\n<p> Similar to the previous assignment, you are expected to experiment with hyperparameter tuning, model and feature selection, and cross-validation to get an optimal accuracy score, which will be evaluated on the leaderboard </p>\n",
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      "name": "Evaluation",
      "content": "<p> Cross-validation, hyper-parameter tuning, and regularisation are encouraged to produce better results. <p>\n\n<p> Evaluation: You will be ranked on your performance on the given test set using accuracy metric. <\\p>",
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    {
      "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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