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(This article was first published on <strong><a href="http://feedproxy.google.com/~r/DataProne-R/~3/NBUY4FkvTVs/real-world-machine-learning-with-R-intro.html"> data prone - R</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<h2>Abstract</h2>
<p>Introduces my Github repo providing R code to accompany the book “Real-World Machine Learning”.</p>
<h2>Introducing <a target="_blank" href="https://github.com/padamson/rwml-R">rwml-R</a></h2>
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<p>The book “Real-World Machine Learning” attempts to prepare the reader for the<br>
realities of machine learning. It covers a basic framework for<br>
machine-learning projects, then it dives into extended examples that show how<br>
that basic framework can be applied in realistic situations. It attempts to<br>
provide the “hidden wisdom” on how to go about implementing products and<br>
solutions based on machine learning. The book is a relatively easy read and<br>
definitely worth the investment in time, but all of the supplied code is<br>
contained in<br>
iPython notebooks. I’m working through the book, reproducing all of the code<br>
listings and figures using R markdown, and I’m posting<br>
the results in a github repo: <a target="_blank" href="https://github.com/padamson/rwml-R">rwml-R</a>.<br>
If you find this project helpful, find any errors, or have any suggestions,<br>
please leave a comment below or use the Tweet button.</p>
<h2>Example: Mosaic Plot in Figure 2.12</h2>
<p>To reproduce the mosaic plot in Figure 2.12 of the book, I use the <a target="_blank" href="https://cran.r-project.org/web/packages/vcd/vcd.pdf">vcd</a><br>
package which contains a plethora of excellent tools for exploring categorical<br>
data. The below mosaic plot shows the relationship between passenger gender<br>
and survival in the supplied Titanic Passengers dataset.</p>
<p></p>
<p><img alt="Plot generated by above code" src="https://i0.wp.com/padamson.github.io/assets/figure2_12.png?w=456" data-recalc-dims="1"></p>
<h2>Feedback welcome</h2>
<p>I’d love to hear from you if you find this project helpful or if you<br>
have any suggestions. Please leave a comment below or use the Tweet button.<br>
Also, feel free to fork the <a target="_blank" href="https://github.com/padamson/rwml-R">rwml-R repo</a> and<br>
submit a pull request if you want to contribute.</p>
<p><a data-style="mega" target="_blank" href="https://github.com/padamson/rwml-R/archive/master.zip" data-icon="octicon-cloud-download">Download</a><br>
<a data-style="mega" data-count-href="http://feedproxy.google.com/padamson/rwml-R/network" data-count-aria-label="# forks on GitHub" target="_blank" data-count-api="/repos/padamson/rwml-R#forks_count" href="https://github.com/padamson/rwml-R/fork" data-icon="octicon-repo-forked">Fork</a></p>
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