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  "source_url": "https://www.kaggle.com/competitions/automlsydney/data",
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      "name": "rules",
      "content": "\n\t<!-- 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.\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>There is no maximum team size.</p>\n    <h3>Submission Limits</h3>\n    <p>You may submit a maximum of 2 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>09/11/2018 12:00 AM UTC</strong></p>\n    <p>Merger Deadline: <strong>None</strong></p>\n    <p>Entry Deadline: <strong>None</strong></p>\n    <p>End Date: <strong>16/11/2018 12:00 AM UTC</strong></p>\n\t<!-- End Base Rules -->",
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      "content": "<p>The data used from this challenge is sourced from the ACNC Annual Information Statements of Fiscal Years 2014, 2015, 2016. </p>\n<p>What is the ACNC? The Government's big non profit sector regulator. In exchange for tax free status, charities register with the ACNC, and provide an Annual Information Statement (AIS). The AIS helps protect the sector against corruption, and increase donor confidence. </p>\n<p> We are predicting the donations and bequests that a charity will earn for a given year. </p>\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>\nThis is documented in the EDA Kernal. ",
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      "name": "Description",
      "content": "<h1>Mankind Vs Machine Sydney Prediction Challenge</h1>\n<h2>Introduction</h2>\n<p>The <b>Mankind vs Machine</b> competition requires data for either bespoke or automated predictions to be compared.  <br>\nI decided it would be great if this data and competition could tie into a broader social purpose, and introduce everybody to an awesome open dataset and a compelling problem. <br>\n<b>THE OPEN DATASET:</b> The Australian Charities and Not for Profit Commission (ACNC) Annual Information Statement, containing activity, beneficiary, and financial information for all registered charities in Australia. Data has been combined over 2014, 2015, and 2016.<br>\n<b>THE PROBLEM:</b> Optimisation of government grants to the non profit sector (which types of services need it most)? <br>\n<b>A POSSIBLE SOLUTION:</b> Creating an estimation of a given charity's income from donations/bequests/etc. <br>\n<b>THE TARGET FEATURE:</b> <i>[donations_and_bequests]</i>, which is the charity's reported income from donations or bequests for a given fiscal year. <br>\n<b>THE EVALUATION METRIC:</b> <i>Root Mean Square Error</i> on the log(1+x) transformed prediction, where x is the predicted target feature <i>[donations_and_bequests]</i><br>\n<b>THE PRIZE:</b> <i>GLORY</i> and Swag from GA <i>\n</p>\n<p> More about this competition, and an intro to the AutoML service TPOT, can be found using these links. Over time, more and more information will be available in the competition Kernals. <br>\n<b>Intro Talk:</b> http://bit.ly/2SVkFpw \n<br>\n<b>Github repo with TPOT example:</b> http://bit.ly/2qAwaWz \n<br>\n</p>\n<h2>Acknowledgements</h2>\n<p>We thank the ACNC for making this dataset available!</p>",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is the <a href=\"https://en.wikipedia.org/wiki/Root-mean-square_deviation\"> Root Mean Square Error</a> of the log(x+1) transformed prediction target donations and bequests (x).",
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