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  "fetched_at": "2026-10-25T00:56:03Z",
  "source_url": "https://www.kaggle.com/competitions/corrected-bike-sharing-regression-otm-714-2022/data",
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      "name": "data-description",
      "content": "<p>You will be provided with three data files. The first is a training set, which gives usage data for the first 13 days of each month over 2 years, broken down by hour and by type of user. The second is a test set, which gives all of the \"X\" variable data for days 14 through 19 of each month, but does not include actual bike usage. You will try to forecast usage for these days/times. The third is a sample submission file that you can use as a template for submitting your forecasts.\n\n<h2>File descriptions</h2>\n<ul>\n<li><strong>bike sharing training for students.csv</strong> - the training set</li>\n<li><strong>bike sharing test for students.csv</strong> - the test set</li>\n<li><strong>bike sharing sample submission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n\n<h2>Data Description</h2>\n<ul>\n<li><strong>datetime</strong> - hourly date + timestamp</li>  \n<li><strong>season</strong> -  1 = spring, 2 = summer, 3 = fall, 4 = winter</li> \n<li><strong>holiday</strong> - whether the day is considered a holiday</li>\n<li><strong>workingday</strong> - whether the day is neither a weekend nor holiday</li>\n<li><strong>weather - </li>\n<p>   1: Clear, Few clouds, Partly cloudy, Partly cloudy</p> \n<p>   2: Mist + Cloudy, Mistt + Broken clouds, Mist + Few clouds, Mist</p> \n<p>   3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds</p>\n<p>   4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog</p> \n<li><strong>temp</strong> - temperature in Celsius</li>\n<li><strong>atemp</strong> - \"feels like\" temperature in Celsius</li>\n<li><strong>humidity</strong> - relative humidity</li>\n<li><strong>windspeed</strong> - wind speed</li>\n<li><strong>casual</strong> - number of non-registered user rentals initiated</li>\n<li><strong>registered</strong> - number of registered user rentals initiated</li>\n<li><strong>count</strong> - number of total rentals</li>\n</ul>\n\n<h2>Acknowledgements</h2>\n<p>This data set was part of an earlier public Kaggle competition.</p> \n",
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      "name": "Description",
      "content": "<p>This assignment will take the form of an in-class competition to forecast demand for bikes in a bike-sharing system. You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 13 days of each month, while the test set is the 14th through the 19th of the month. You must predict the total count of bikes rented during each hour covered by the test set. Your forecasts will be assessed based on RMSE.</p>\n\nHere is a handy link to Kaggle's [competition documentation](https://www.kaggle.com/docs/competitions), which includes, among other things, instructions on [submitting predictions](https://www.kaggle.com/docs/competitions#making-a-submission).\n\n",
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      "name": "Evaluation",
      "content": "<h2>Evaluation Metric</h2>\n<p>The evaluation metric for this competition is root mean squared error (RMSE) of the submitted forecasts.\n\n</p><h2>Submission Format</h2>\n<p>For every date and time in the test set (i.e., for the 14th of the month to the 19th of the month for both 2011 and 2012, and for every hour of the day), submission files should be CSV files containing two columns (with headers):  datetime and count. A sample submission file is provided with the data set.</p>\n",
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    {
      "name": "rules",
      "content": "The general rules about academic integrity that apply to this course also apply to this competition. Below are some points of emphasis, but these should not be interpreted to replace the standard academic integrity policies. If you have any questions about what is or is not allowed for this assignment/competition, please ask.  \n\n - Every student should register for an account on [Kaggle](kaggle.com). (It's free.)\n - You may use books, the internet, or other \"arms length\" sources for general assistance in running linear regression models. However, you may not seek any assistance (including from \"arms length sources\") that is directly related to this forecasting problem.\n - You may not seek direct assistance from individuals - e.g., classmates, relatives, friends, people you have worked with, students who have taken this course in the past, etc.\n - You should work only within your team, and should not share any information with others in the class.\n - You are allowed up to 10 submissions per calendar day.\n - In the final rankings, your best 2 submissions (based on the public leader board) will be used in evaluating your score.\n - For each submission, 30% of the test data will be used to provide a score for the public leader board. For final evaluation, the remaining 70% of the data will be used to provide a score for the final (private) leader board.",
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