{
  "fetched_at": "2026-10-20T03:53:02Z",
  "source_url": "https://www.kaggle.com/competitions/coderit/data",
  "pages": [
    {
      "name": "rules",
      "content": "Basic rules of a competition:\n\n - Don't cheat!\n - Have Fun!\n - More Importantly, Don't cheat. Creator was awake will 3AM setting up this competition and he would appreciate the honesty and some sleep",
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      "name": "data-description",
      "content": "<p>The train.csv file contains the training data that must be used to train your models. The test.csv file contains the data that you must use to predict. The sampleSubmission.csv file describes how the submission file is supposed to be like when uploaded to the kaggle platform. The \"Category\" attribute is the one to be predicted. It describes the type of forest covered described by the various attributes.</p>\n<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<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>Elevation</strong> - Elevation in meters</li>\n<li><strong>Aspect</strong> - Aspect in degrees</li>\n<li><strong>Slope</strong> - Slope in degrees</li>\n<li><strong>Horizontal_Distance_To_Hydrology</strong> -  Horizontal Distance to nearest surface water</li>\n<li><strong>Vertical_Distance_To_Hydrology</strong> - Vertical Distance to nearest surface water</li>\n<li><strong>Horizontal_Distance_To_Roadways</strong> - Horizontal Distance to nearest roadway</li>\n<li><strong>Hillshade_9am (0 to 255 index)</strong> - Hillshade index at 9am</li>\n<li><strong>Hillshade_Noon (0 to 255 index)</strong> - Hillshade index at noon</li>\n<li><strong>Hillshade_3pm (0 to 255 index)</strong> - Hillshade index at 3pm</li>\n<li><strong>Horizontal_Distance_To_Fire_Points</strong> - Horizontal Distance to nearest wildfire ignition points</li>\n<li><strong>Wilderness_Area (4 binary columns, 0 = absence or 1 = presence)</strong> - Wilderness area designation</li>\n<li><strong>Soil_Type (40 binary columns, 0 = absence or 1 = presence)</strong> - Soil Type designation</li>\n<li><strong>Category(7 types, integers 1 to 7) </strong> -Forest Cover Type designation</li>\n</ul>",
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      "name": "Description",
      "content": "<p>The first competition and an introduction to data science starts with prediction of the type of forest cover. There are multiple attributes that may describe a certain location's forest cover. This first competition you will be using various attributes such as elevation, distance from water, soil type, etc in order to predict the area's forest cover type.</p>\n\n![LOOK TREES !!!!!!!!][1]\n\n\n  [1]: https://travel.amerikanki.com/wp-content/uploads/2014/01/Most-Beautiful-Forests-Around-the-World.jpg",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation page describes how submissions must be formatted.</p>\n<hr />\n<h2>Submission Format</h2>\n<p><strong>For every Id</strong>, submission files should contain a column: Category.</p>\n<p>The file should contain a header and have the following format:</p>\n<p>Where the Id represents an entry in the testing set and the Category is the predicted type of forest cover</p>\n<pre>Id,Category<br />1,3<br /><span style=\"font-size: 14px; line-height: 1.4em;\">2,4<br />3,1<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">4,2<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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