{
  "fetched_at": "2026-10-20T03:38:48Z",
  "source_url": "https://www.kaggle.com/competitions/dsir-830-project-2-regression-challenge/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.</p>\r\n<p>Participants should be able to answer questions like these after reading the data description: What files do I need? What should I expect the data format to be? What am I predicting? What acroynms will I encounter?</p>\r\n<h2>File descriptions</h2>\r\n<ul>\r\n<li><strong>train.csv</strong> - the training set</li>\r\n<li><strong>test.csv</strong> - the test set</li>\r\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\r\n<li><strong>metaData.csv</strong> - supplemental information about the data</li>\r\n<li>etc...</li>\r\n</ul>\r\n<h2>Data fields</h2>\r\n<ul>\r\n<li><strong>customer_id</strong> - an anonymous id unique to a given customer</li>\r\n<li><strong>movie_id</strong> - the id of a movie</li>\r\n<li><strong>movie_title</strong> - the title of a movie</li>\r\n<li>etc...</li>\r\n</ul>",
      "isPublished": false,
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    {
      "name": "Description",
      "content": "Welcome the Kaggle challenge for Project 2! As part of a successful submission for Project 2, we will expect you to make **at least one** (and hopefully, multiple!) submissions towards this regression challenge.\n​\nIn this challenge, you will use the well known Ames housing data to create a regression model that predicts the price of houses in Ames, IA. You should feel free to use any and all features that are present in this dataset.\n​\n# Goal\nIt is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable.\n",
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    },
    {
      "name": "Evaluation",
      "content": "User submissions are evaluated by comparing their Submission CSV to the ground truth Solution CSV with respect to a chosen Scoring Metric, for example the Root-mean-squared Error (RMSE) metric.\n\nEach Scoring Metric will expect the Solution and Submission CSVs to have certain columns, defined in a header row. For example, RMSE expects an Id column identifying the row and an Expected column specifying the true value which competitors are attempting to predict. Your Solution CSV's header row does not need to use the exact column names, you will be able to map your column names onto the Metric's expected columns.\n\nTo ensure that competitors cannot game the system, the rows in the solution file are sampled into Public and Private rows. Public scores are shown on the public leaderboard, while only the private scores are used to determine the competition winner. The sampling can be done manually by including a Usage column with values of Public, Private, or Ignored. Otherwise, rows will automatically be sampled into these buckets.",
      "isPublished": false,
      "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!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
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    }
  ]
}