{
  "fetched_at": "2026-10-20T14:49:33Z",
  "source_url": "https://www.kaggle.com/competitions/assignment2-ecs308-658/data",
  "pages": [
    {
      "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</ul>\n\n\n<h2>Data fields</h2>\n<ul>\n<li><strong>id</strong> - date in (m/d/y format)</li>\n<li><strong>value</strong> - real valued (actual quantity will be announced later)</li>\n</ul>\n\n<h2>Submission format</h2>\nid,value <br>\n5/1/10,0.28",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "<p> In this assignment, you will be trying to model a real-world time-series data set. </p> \n\n<p> This contains measurements of a certain quantity at different points in time. The train set contains 110 time points; and the test set contains another 10 points for which the measured value has been removed. In each row, the first value is the date of the measurement (in US format, so month comes before day), and the second value is the actual measurement. Your task is to train a linear regression model (using appropriate basis functions) which can predict the missing values as accurately as possible. </p>\n\n<p> In other words, this task requires you to predict the values on 10 days based on the given information for 110 days. </p>\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "<p> You are allowed to use only linear regression models for this task. 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 that test set using mean-squared error (MSE) metric. <\\p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "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!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}