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  "fetched_at": "2026-10-19T20:07:50Z",
  "source_url": "https://www.kaggle.com/competitions/genesis-usict/data",
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      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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      "name": "data-description",
      "content": "<p> In this competition you are required to predict the quantity of the house. The test and train data has been divided into 2 parts which contain different attribute related to the house. Authors have to merge them according to their own analysis to create a train and test data.\n</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>etc...</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>House no</strong> - an anonymous id unique to a given house</li>\n<li><strong>no,bathroom</strong> - the number of bathroom a house have</li>\n<li><strong>house_class</strong> - the class to which the house belongs</li>\n<li>etc...</li>\n</ul>\n",
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      "name": "Description",
      "content": "<p>You have some experience with R or Python and machine learning basics, but you’want to test your skills this might be a great opportunity for you. This competition is the perfect introduction to techniques like data extraction an analyzing it to apply an apt machine learning model.</p>\n\n\nAs a machine learning event of InfoXpression 2018, this competition is aimed at brainstorming the participant to use various techniques to find the best solution.  \n</p>\n<p>\nThis data-set has been collected during a house assessment survey of the Western region.\nThe participants have to predict the quantity depending on the various features of the house.\n\n**To be eligible for prizes kindly register yourself [here][1].**\n\n\n  [1]: http://bit.ly/genesisusict",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/Mean_absolute_error\"> \nMean absolute error</a>. \nMAE is a measure of difference between two continuous variables. Assume X and Y are variables of paired observations that express the same phenomenon.\n</p>\n\n<p><strong>For every author in the dataset</strong>, submission files should contain two columns: House No and Quality. DuplicateAuthorIds should be a space-delimited list.\n<p>The file should contain a header and have the following format:</p>\n<pre>House_no,Quality<br />1,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">8,8<br />9,10<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">10,9<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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