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<!DOCTYPE html>
<html>
<head>
<title>Predicting Happiness</title>
<meta charset="utf-8">
<meta name="description" content="Predicting Happiness">
<meta name="author" content="Alexander Savi">
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<slides class="layout-widescreen">
<!-- LOGO SLIDE -->
<slide class="title-slide segue nobackground">
<hgroup class="auto-fadein">
<h1>Predicting Happiness</h1>
<h2>Pitch for course project Data Science - Developing Data Products</h2>
<p>Alexander Savi<br/></p>
</hgroup>
<article></article>
</slide>
<!-- SLIDES -->
<slide class="" id="slide-1" style="background:;">
<hgroup>
<h2>Disclaimer</h2>
</hgroup>
<article data-timings="">
<p>This presentation is part of a project for the Data Science Developing Data Products track from Coursera.</p>
<p>The course can be found at: <a href="https://class.coursera.org/devdataprod-002">https://class.coursera.org/devdataprod-002</a></p>
<p>The Shiny app this pitch refers to can be found at: <a href="https://aosavi.shinyapps.io/datsci-devdatprod/">https://aosavi.shinyapps.io/datsci-devdatprod/</a></p>
</article>
<!-- Presenter Notes -->
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<slide class="" id="slide-2" style="background:;">
<hgroup>
<h2>Shiny app</h2>
</hgroup>
<article data-timings="">
<p>A Shiny app was built and hosted on shinyapps.io, see <a href="https://aosavi.shinyapps.io/datsci-devdatprod/">https://aosavi.shinyapps.io/datsci-devdatprod/</a></p>
<p>This app uses a `state-of-the-art' algorithm to quantify happiness as a result of a sleep drug taken and the side of the bed one gets out.</p>
<p>The user may select the NotSoSuperSleep(TM) drug or the SuperSleep(TM) drug, and the wrong or the right side of the bed. From these to predictors an 'insanely accurate' prediction of happiness (informal studies) is calculated.</p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-3" style="background:;">
<hgroup>
<h2>Technology</h2>
</hgroup>
<article data-timings="">
<p>The algorithm takes the mean amount of extra sleep that participants from two empirical samples had after taking one of two different drugs (see help(sleep) in R for details).</p>
<p>The means are then divided by either a 'wrong side' factor (2) or a 'right side' factor (1/2).</p>
<p>The algorithm spits out the predicted happiness for each of the 4 circumstances. Please note that a higher prediction refers to more happiness.</p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-4" style="background:;">
<hgroup>
<h2>Example</h2>
</hgroup>
<article data-timings="">
<p>An example prediction is shown here, where the SuperSleep(TM) drug is taken, but unfortunately the wrong side of the bed was used.</p>
<pre><code class="r">drug <- "SuperSleep(TM)"
side <- "Wrong side"
</code></pre>
<pre><code class="r">data(sleep)
expHappiness <- function(drug, side) {
if(drug=="NotSoSuperSleep(TM)") drug <- 1
if(drug=="SuperSleep(TM)") drug <- 2
if(side=="Wrong side") side <- 2
if(side=="Right side") side <- 1/2
mean(sleep[sleep[,"group"]==drug, "extra"]) / side
}
pred <- expHappiness(drug, side)
</code></pre>
<p>The predicted happiness the following day is a rather suboptimal 1.165.</p>
</article>
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