{
  "fetched_at": "2026-10-20T05:44:12Z",
  "source_url": "https://www.kaggle.com/competitions/enpm808w-fall-2020-hw2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\nFile descriptions\n\nhouse_train.csv - the training set house_test.csv - the test set housing-sample2.csv - a sample submission file in the correct format Data fields\n\nid - A unique ID given to each area zip - The zip code associated with the area state - The state the area is in county - The county the area is in poverty - The proportion of the population living in poverty in 2007 price2007- The average home price in the area in 2007 price2013 - The average home price in the area in 2013",
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    {
      "name": "Description",
      "content": "The Evaluation metric is RMSE. This rewards you for getting close to the true answer (and penalizes being very far away).\n\nSubmission Format\n\nFor every id in the test dataset, your answer submission file should have only two columns: id and price2013. The price2013 should reflect the prediction based on the information from that id in the house_test.csv file.\n\nThe file should contain a header and have the following format:\n\nid,prediction\n6,265600\n13,79900\n20,332000\n30,98700\n37,266100\n67,198300\netc.",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:</p>\n<hr>\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean 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 author in the dataset</strong>, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids.&nbsp;For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>AuthorId,DuplicateAuthorIds<br>1,1<br></pre>",
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      "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!",
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