{
  "fetched_at": "2026-10-20T05:27:59Z",
  "source_url": "https://www.kaggle.com/competitions/2018fallban7002/data",
  "pages": [
    {
      "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>\nYou are given a three datasets with “train”, “test” and “score”.  your job is to train a model to predict wine quality \n(0 = lowest quality, 10 = highest quality) using the “train” data set. You’ll evaluate it against the “test” data set and apply (aka score) it to the score data set .  **.</p>\n\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set use this to build your regression model with</li>\n<li><strong>test.csv</strong> - the test set use this to evaluate your regression model</li>\n<li><strong>scorecsv</strong> - the data set used to score your regression model</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format just two columns: id, quality</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n\n<li><strong>type </strong> - type of wine (red or white)</li>\n<li><strong>part</strong> - partition TRAIN, TEST, or SCORE</li>\n<li><strong>id</strong> - id of the wine </li>\n<li><strong>white_flag </strong> - flag 1 = white, 0 = red </li>\n<li><strong>red_flag </strong> - flag 1 = red, 0 = white </li>\n<li><strong>fixed_acidity </strong></li>\n<li><strong>volatile_acidity</strong></li>\n<li><strong>citric_acid</strong></li>\n<li><strong>residual_sugar</strong></li>\n<li><strong>chlorides</strong></li>\n<li><strong>free_sulfur_dioxide</strong></li>\n<li><strong>total_sulfur_dioxide</strong></li>\n<li><strong>density</strong></li>\n<li><strong>pH</strong></li>\n<li><strong>sulphates</strong></li>\n<li><strong>alcohol</strong></li>\n<li><strong>quality</strong> - this is your regression target !</li>\n\n</ul>",
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    {
      "name": "Description",
      "content": "\nThis week we are going to do something completely different, and take a look at regression and “estimation” tasks with PROC REG and PROC SCORE. \n\nRegression and estimation is a big topic and while linear regression is a bread-and-butter tool, there are a number of other methods that will allow you to perform regression tasks. My goal is to show you the mechanics and process necessary for estimation/regression modeling , i.e the LAZY Data Scientist’s approach for modeling, and leave the details to future classes.",
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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). </p>\n<hr />\n<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/Root-mean-square_deviation\"> RMSE</a>. ",
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