{
  "fetched_at": "2026-10-20T04:30:27Z",
  "source_url": "https://www.kaggle.com/competitions/comp180bikeshare/data",
  "pages": [
    {
      "name": "rules",
      "content": "<!-- Begin Base Rules -->\n    <h3>One account per participant</h3>\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n    <h3>No private sharing outside teams</h3>\n    <p>\n        Privately sharing code or data outside of teams is not permitted. Verbal help is fine, but no copy/paste.\n        It's okay to share small code snippets if made available to all participants on the forums.\n    </p>\n\n    <h3>Team Limits</h3>\n    <p>The maximum team size is 3 people.</p>\n    <h3>Submission Limits</h3>\n    <p>You may submit a maximum of 10 entries per day.</p>\n    <p>You may select up to 2 final submissions for judging (the best 2 are chosen for you by default).</p>\n    <h3>Competition Timeline</h3>\n    <p>Start Date: <strong>9am, November 3, 2018</strong></p>\n    <p>Entry Deadline: <strong>9am, December 7, 2018</strong></p>\n    <p>End Date: <strong>9am, December 7, 2018</strong></p>\n<!-- End Base Rules -->",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 16 days of each month, while the test set is the 17-19th day of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period. That is, predict \"count\" without using \"count\" or it's components \"casual\" and \"registered\".\n\n\nData Fields\n\n\n* **datetime** - hourly date + timestamp  \n* **season** -  1 = spring, 2 = summer, 3 = fall, 4 = winter \n* **holiday** - whether the day is considered a holiday\n* **workingday** - whether the day is neither a weekend nor holiday\n* **weather** -  \n    * 1 - Clear, Few clouds, Partly cloudy, Partly cloudy\n    * 2 -  Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n    * 3 - Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds  \n    * 4 - Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \n* **temp** - temperature in Celsius\n* **atemp** - \"feels like\" temperature in Celsius\n* **humidity** - relative humidity\n* **windspeed** - wind speed\n* **casual** - number of non-registered user rentals initiated\n* **registered** - number of registered user rentals initiated\n* **count** - number of total rentals\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "The goal of this competition is to predict bike share use, given the hour, day, and information about the weather. Companies like Divvy try to predict how much demand there will be for bikes on any give day to allocate resources to redistribute bikes so that, ideally, very few bike stations are ever full (when you can’t park your bike) or empty (when you can’t pick up a bike if you want to).\n\nThe [data tab][1] provides detailed information on the data set and necessary downloads.\n\nSee the class google doc page on details about teams, grading, and updated tips.\n\n\n  [1]: https://www.kaggle.com/c/comp180bikeshare/data",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "Submission Format\n\nYour submission file must have a header line and should be structured in the following format:\n\n     datetime,count\n     2011-01-20 00:00:00,0 \n     2011-01-20 01:00:00,0\n     2011-01-20 02:00:00,0\n     ...\n\nThe tutorial code should demonstrate how to generate such a file from a very simple prediction model. Note, these prediction are to be done on the test file under the data tab, where you do not know the actual count, and should match the rows of the test file in count and order. \n\nYour predictions will be scored by comparing to the ground truth information submitted when this competition was created. Submissions are evaluated using Root Mean Squared Error (RMSE).\n\n\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}