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  "fetched_at": "2026-10-20T09:48:03Z",
  "source_url": "https://www.kaggle.com/competitions/bids-machine-learning/data",
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      "name": "rules",
      "content": "<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.\n        It's okay to share code if made available to all participants on the forums.\n    </p>\n    <h3>Team Mergers</h3>\n    <p>Team mergers are not allowed in this competition.</p>\n\n    <h3>Team Limits</h3>\n    <p>For this competition, no teams are permitted. The work is individualized.</p>\n    <h3>Submission Limits</h3>\n    <p>You may submit up to 20  entries per day.</p>\n    <p>You may select up to 2 final submissions for judging.</p>\n    <h3>Competition Timeline</h3>\n    <p>Start Date: <strong>12/01/2020</strong></p>\n    <p>End Date: <strong>31/01/2020 12:00 PM UTC</strong></p>\n\t\n",
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      "content": "<h1> Bike Sharing </h1>\n\n<p>Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. </p>\n<p>This is a real world dataset obtained from a bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. </p>\n<p>Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city.</p>\n<p>Your mission is to try to predict the number of bikes being rented each day using the provided variables.</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</ul>\n<h2>Data fields</h2>\n<p> Please check the field description on the train dataset (click the train.csv link on the \"Data Sources\" and you will see it on \"Columns\").\n",
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      "name": "Description",
      "content": "<p>Hello and welcome to our first Kaggle class exercise. Here we will practice all what we had learn until today. In this exercise we will practice the algorithms for predicting regression outcomes. You can use R or Python to calculate your predictions.</p>\n<p/>\n<h2> Acknowledgements </h2>\n<p>This dataset.was obtained from the <a href=\"http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset\"> UCI Machine Learning Repository </a></p>\n<p>The dataset was kindly donated by Hadi Fanaee-T from the Laboratory of Artificial Intelligence and Decision Support (LIAAD), University of Porto, Portugal</p>",
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      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/marknagelberg/rmsle-function\">Root Mean Squared Logarithmic Error</a> . Because this value is not only an absolute value, this metric is one of the most resistant metric to outliers that's possible using simple methods. Thus it is the most fair measure to calculate the number of bikes on the given data.</p>\n\n<p>The calculation of this measure is made as follows:</p>\n\n<p>\n\\[Root Mean Squared Logarithmic Error=\\sqrt{ {1/n}\\sum( { \\log(\\hat{y_0} + 1)−log(y_0 +1) )}^2 }\\]\n</p>\n\n\n<p>In Python you can calculate it as :<p> \n<pre>\nimport math\n\n#A function to calculate Root Mean Squared Logarithmic Error (RMSLE)\ndef rmsle(y, y_pred):\n\tassert len(y) == len(y_pred)\n\tterms_to_sum = [(math.log(y_pred[i] + 1) - math.log(y[i] + 1)) ** 2.0 for i,pred in enumerate(y_pred)]\n\treturn (sum(terms_to_sum) * (1.0/len(y))) ** 0.5\n\nrmsle(y, y_pred)\n</pre>\n<p> In R you can calculate it as :</a>\n<pre>\nrmsle  < - function(y,ypred) {\n    return(sqrt(mean((log(y+1,2) - log(y_pred+1,2))^2))\n}\n</pre>\n<p>Submission Format\nFor every row in the dataset, submission files should contain two columns: id and cnt. It should be a comma-delimited list.</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>\nid, cnt\n1, 0\n2, 0\n3, 0\n4, 0\netc.\n</pre>\n",
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