{
  "fetched_at": "2026-10-20T10:08:13Z",
  "source_url": "https://www.kaggle.com/competitions/cs419m/data",
  "pages": [
    {
      "name": "rules",
      "content": "\n\t<!-- Begin Base Rules -->\n    <h3>One account per participant</h3>\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n    <h3>No private sharing outside teams</h3>\n    <p>\n        Privately sharing code or data outside of teams is not permitted.\n    </p>\n    <h3>Team Mergers</h3>\n    <p>Team mergers are not allowed in this competition.</p>\n\n    <h3>Team Size</h3>\n    <p>Maximum team size allowed is <b>two</b>.</p>\n    <h3>Submission Limits</h3>\n    <p>You may submit a maximum of 10 entries per day.</p>\n    <p>You may select up to 2 final submissions for judging.</p>\n    <h3>Competition Timeline</h3>\n    <p>Start Date: <strong>30 July 2018  05:00 PM</strong></p>\n    <p>End Date: <strong>14 August 2018 11:55 PM</strong></p>\n\t<!-- End Base Rules -->\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "<p class=\"small-heading\"><b>Data Set Information:</b></p>\n<p class=\"normal\">The dataset comprises 768 samples and 8 features, aiming to predict a real valued responses.</p>\n\n<br />\n\n<p class=\"small-heading\"><b>Attribute Information:</b></p>\n<p class=\"normal\">The dataset contains eight attributes (or features, denoted by X1...X8) and one response (denoted by output). The aim is to use the eight features to predict the response.<br>\nX1\tRelative Compactness <br>\nX2\tSurface Area <br>\nX3\tWall Area <br>\nX4\tRoof Area <br>\nX5\tOverall Height <br>\nX6\tOrientation <br>\nX7\tGlazing Area <br>\nX8\tGlazing Area Distribution<br><br>\n\nX6(Orientation) i and X8(Glazing Area Distribution) take integer values . All the other attributes are continuous and real valued. \n\n</p>\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "<p>In this assignment, you will be implementing a regression tree from scratch. Each decision node will correspond to one of the features in train.csv, which is selected by choosing the feature that minimises loss. The features in train.csv and test.csv are either continuous-valued (i.e. falling within a range) or discrete-valued (i.e. one of N values). The competition uses mean squared loss for evaluation</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "<hr />\n<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/Mean_squared_error#Predictor\" > Mean Squared Loss</a>.\n\n<h2>Submission Format</h2>\n<p>Your submission file should contain two columns: Id,output . These two columns are comma separated(.csv file with two columns)</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>Id,output<br />1,4.1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">2,1.2 <br />3,5.3<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">4,7.4<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}