{
  "fetched_at": "2026-10-19T15:29:11Z",
  "source_url": "https://www.kaggle.com/competitions/edld-654-spring-2020/data",
  "pages": [
    {
      "name": "rules",
      "content": "Work as a team, be respectful of each other, and have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "Please see the data dictionary for specific column definitions, and the overview tab for an overall overview of the data.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "# Statewide testing data\nWelcome to the homepage for our class competition. I've uploaded a data dictionary to go along with the data itself, but please don't hesitate to get in contact with Joe or I if you have questions.\n\nAs a basic overview, students in every state across the nation are tested annually in reading and math in grades 3-8.  These specific data are simulated from an actual statewide testing administration across the state of Oregon, but the overall distributions are highly similar. The school IDs are real, and you should be able to use these IDs (specifically the `ncessch`) to link the data with other sources (for example, see the [National Center for Educational Statistics](https://nces.ed.gov/ccd/)). Because you have the longitude and latitude, you could also consider linking the data with census data to obtain information on, for example, the economic conditions of the surrounding area around the school (you would have to do this with a spatial join, most likely).",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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      "mimeType": "",
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    },
    {
      "name": "Evaluation",
      "content": "# Root Mean Square Error\n\nThis is pretty straightforward. We are in a regression setting and we're using one of the most basic evaluation metrics, the root mean square error.\n\n$$\nRMSE = \\sqrt{\\frac{1}{n} \\Sigma(y\\_i -\\hat{ y\\_i})^2 }\n$$",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Timeline",
      "content": "# Due Dates\nYou are allowed to submit predictions whenever you'd like. However, you MUST submit predictions on the following dates. Please comment these submissions with their purpose.\n\n* Preliminary Fits 1: Due 4/29/20\n* Preliminary Fits 2: Due 5/20/20\n* Final Fits: Due 6/10/20 (these should be from the model you discuss in your blog post)",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Prizes",
      "content": "# Extra Credit\nUp to five points extra credit will be awarded to the team providing the most performant model. If there are ties amongst teams, extra credit will be awarded at the discretion of the instructor.\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}