{
  "fetched_at": "2026-10-19T08:33:17Z",
  "source_url": "https://www.kaggle.com/competitions/bgurcourse2019/data",
  "pages": [
    {
      "name": "rules",
      "content": "\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!\n",
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    {
      "name": "data-description",
      "content": "<p>This page shows up underneath the data files. We have provided: train, test and an example for a submission file. The train data includes the parameters described below. Your goal is to predict the instant air temperature. The instant air temperature is the value measured at the given time. The test file does not include the instant air temperature it is for you to predict. The test data also includes an id for each observation. The id will be used in the submission file. The submission file will include two columns, the first is the id of the observation and the second is the predicted value for the  instant air temperature</p>\n\n<p>\n\n\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n\n</ul><h2>Data fields</h2>\n<ul>\n\n</p><h2>The climate parameters include: </h2>\n<ul>\n<li>Instant Air Temperature (Celsius degrees) </li>\n<li>Instant Air Temperature (Celsius degrees) </li>\n<li>Maximum Air Temperature (Celsius degrees) </li>\n<li>Minimum Air Temperature (Celsius degrees) </li>\n<li>Relative Humidity of Air (%)</li>\n<li>Maximum Relative Air Humidity (%)</li>\n<li>Minimum Relative Air Humidity (%) </li>\n<li>Instant Dew Point (Celsius degrees) </li>\n<li>Maximum Dew Point (Celsius degrees)  </li>\n<li>Minimum Dew Point Temperature (Celsius degrees) </li>\n<li>Instant Air Atmospheric Pressure (millibars)</li>\n<li>Maximum Air Atmospheric Pressure (millibars)</li>\n<li>Minimum Air Atmospheric Pressure (millibars) </li>\n<li>Instant Wind Speed (meters per second)  </li>\n<li> Wind Direction (radius degrees)</li>\n<li>Wind Gust Intensity (meters per second)  </li>\n<li>Solar radiation   </li>\n<li>Precipitation (millimeters)   </li>\n</strong></ul><strong>\n\n<p>\nThe data was taken hourly from different weather stations in South America. Therefore information regarding the time, date and location of the station is also available.  </p>\n</ul>\n",
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    {
      "name": "Description",
      "content": "<p>\nUse the climate parameters available measured from different weather stations to predict the Instant Air Temperature at a given time.</p>\n<p>\nThe data was taken hourly from different weather stations in South America. Therefore information regarding the time, date and location of the station is also available.  </p>\n<p>\nThe data is incomplete, some of it was omitted or not recorded, you need to think how to can it be handled </p>\n\n\n<p>\nYou can submit up to 2 predictions a day. The public score is the MSE of 30% of the test data.\nAn example for a submission is presented in the data section.\n</p>\n\n\n",
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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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