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      "name": "Description",
      "content": "<p><span style=\"line-height: 1.4em;\">Data Science London is </span><a href=\"http://www.meetup.com/Data-Science-London/events/105840372/\" style=\"line-height: 1.4em;\">hosting a meetup on Scikit-learn</a><span style=\"line-height: 1.4em;\">.  This competition is a practice ground for trying, sharing, and creating examples of sklearn's classification abilities (if this turns in to something useful, we can follow it up with regression, or more complex classification problems).</span></p>\r\n<p>We encourage participants to post code via the \"Tutorials\" link on the left.  Don't worry about accuracy or whether your code is perfect.  The aim here is to explore sklearn by using it. <span style=\"line-height: 1.4em;\">You do not need to use sklearn to enter the competition. If you're new, we hope you'll use this oppurtunity to practice a new tool.  If you're an expert, we hope you'll share the knowledge and document interesting ways to approach this problem.</span><img src=\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/3428/media/scikit-learn-logo.png\" alt=\"\" style=\"line-height: 1.4em;\" /></p>\r\n<p><span style=\"line-height: 1.4em;\"><strong><a href=\"http://scikit-learn.org/stable/\">Scikit-learn</a></strong> (sklearn) is an established, open-source machine learning library,</span><span style=\"line-height: 1.4em;\"> </span><span style=\"line-height: 1.4em;\">written in Python with the help of NumPy, SciPy and Cython.</span></p>\r\n<p><span>Scikit-learn is very user friendly, has a consistent API, and provides extensive documentation.</span> Its implementation is high quality due to s<span>trict coding standards and high test coverage</span><span>.  Behind sklearn is a very active community, which is </span><span>steadily improving the library.</span></p>\r\n<h2>Meetup Information</h2>\r\n<p>Thursday, March 7, 2013, <span style=\"line-height: 1.4em;\">6:30 PM UTC<br /> <a href=\"http://www.meetup.com/Data-Science-London/events/105840372/\">http://www.meetup.com/Data-Science-London/events/105840372/<br /> </a></span></p>\r\n<p><strong style=\"line-height: 1.4em;\">“Learning in Python with scikit-learn\" by Andreas Mueller</strong></p>\r\n<p><span>This talk will give an overview of the library and introduce general machine learning concepts such as supervised and unsupervised learning, feature extraction, cross validation for model evaluation and hyper parameter selection. We will also touch some more advanced yet practically useful concepts such as feature hashing and ensemble learning.</span></p>\r\n<p><span>Andreas is a PhD student in machine learning an computer vision at Bonn University in Germany. He is one of the core developers and the maintainer of scikit-learn and the author of the blog peekaboo-vision. His interests include principles and applications of machine learning and open science.</span></p>\r\n<p><strong>\"Parallel and large scale learning with scikit-learn\" by Olivier Grisel</strong></p>\r\n<p><span>This talk will give a introduce practical tools and concepts to better leverage multicore machines and small clusters to perform interactive yet scalable predictive modeling with scikit-learn and IPython.parallel. In particular we will introduce:</span></p>\r\n<ul>\r\n<li><span>A short introduction to the parallel features of iPython from the </span>notebook interface</li>\r\n<li>How to perform scalable text feature extraction with the Hashing Trick</li>\r\n<li><span>How to parallelize or distribute model evaluation (cross validation)</span>and hyper parameters tuning</li>\r\n<li>How to optimize memory usage with memory mapping</li>\r\n<li>How to approximate kernel Support Vector Machines for large scale datasets</li>\r\n<li>A short introduction to Ensembles with model averaging and Random Forests</li>\r\n</ul>\r\n<p><span>Olivier is a R&amp;D Software Engineer working in Java</span> by day and a Python machine learning hacker by night. He is interested in applications to Natural Language Processing, Computer Vision and predictive modelling.</p>",
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      "name": "Evaluation",
      "content": "<p>This is a binary classification task, You are evaluated on classification accuracy (the percentage of labels you predict correctly).  The training set has 1000 samples and the testing set has 9000.  Your prediction should be a 9000 x 1 vector of ones or zeros. You also need an Id column (1 to 9000) and should include a header. The format looks like this:</p>\r\n<pre>Id,Solution\r\n1,0\r\n2,1\r\n3,1\r\n...\r\n9000,0\r\n</pre>",
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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>\r\n        Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.<br/>\r\n    </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 10 entries per day.</p>\r\n    <p>You may select up to 5 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>3/6/2013 6:05 PM UTC</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>12/31/2014 11:59 PM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n",
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    {
      "name": "data-description",
      "content": "<p>This is a synthetic data set of 40 features, representing objects from two classes (labeled as 0 or 1). The training set has 1000 samples and the testing set has 9000.</p>",
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