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  "fetched_at": "2026-10-20T11:13:39Z",
  "source_url": "https://www.kaggle.com/competitions/csci688/data",
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      "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>TFI has provided a dataset for a restaurant in the training set, and a test set from the same restaurant on different dates. The data columns include three categories of obfuscated data: Demographic data, Real estate data, and Commercial data. The revenue column indicates a (transformed) revenue of the restaurant in a given year and is the target of predictive analysis. </p>\n\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n<li><strong>metaData.csv</strong> - supplemental information about the data</li>\n<li>etc...</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>P1, P2-P37</strong> - There are three categories of these obfuscated data. Demographic data are gathered from third party providers with GIS systems. These include population in any given area, age and gender distribution, development scales. Real estate data mainly relate to the m2 of the location, front facade of the location, car park availability. Commercial data mainly include the existence of points of interest including schools, banks, other QSR operators.</li>\n<li><strong>revenue</strong> -  The revenue column indicates a (transformed) revenue of the restaurant in a given year and is the target of predictive analysis. Please note that the values are transformed so they don't mean real dollar values. </li>\n\n\n</ul>",
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      "name": "Description",
      "content": "<p>With over 1,200 quick service restaurants across the globe, TFI is the company behind some of the world's most well-known brands: Burger King, Sbarro, Popeyes, Usta Donerci, and Arby’s. They employ over 20,000 people in Europe and Asia and make significant daily investments in developing new restaurant sites.\n\nRight now, deciding when and where to open new restaurants is largely a subjective process based on the personal judgement and experience of development teams. This subjective data is difficult to accurately extrapolate across geographies and cultures. \n\nNew restaurant sites take large investments of time and capital to get up and running. When the wrong location for a restaurant brand is chosen, the site closes within 18 months and operating losses are incurred. \n\nFinding a mathematical model to increase the effectiveness of investments in new restaurant sites would allow TFI to invest more in other important business areas, like sustainability, innovation, and training for new employees. Using demographic, real estate, and commercial data, this competition challenges you to predict the annual restaurant sales of 100,000 regional locations.</p>\n\n<h2>Metrics </h2>\n<p> We use root mean squared error metrics (RMSE).</p>\n\n\n",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is the root mean squared error (RMSE). Compared to the Mean Absolute Error, RMSE punishes large errors:\n<p>\\[RMSE= \\sqrt{1/m \\sum_{i=1}^m (y_i - \\hat{y}_i^2)} \\]</p>\n<p> where y hat is the predicted value and y is the original value.\n</p>\n<h2>Submission Format</h2>\n<p><strong>Submission files should contain two columns: Id and Prediction. \n\nThe file should contain a header and have the following format:</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>Id,Prediction<br />0,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">1,8<br />2,9 <br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">3,10 <br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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