![\"Donald](\"http:\/\/www.jaijuneja.com\/blog\/wp-content\/uploads\/2016\/03\/trump.jpg\")
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Everyone in media wants a piece of the US election pie. On both ends of the political spectrum, traditional party politics are being rocked by new personalities. Voters frustrated with the establishment see this as an opportunity for protest. On the left they’re Feeling the Bern and on the right they’re hoping to Make America Great Again, but in both cases voters have expressed a cocktail of emotions from anger to optimism.<\/p>\n
Front and centre of this circus act sits an orange caricature. To cynics, Donald Trump is a parody of himself, a charlatan who wins support by pandering to public fear. To supporters, he represents the anti-establishment and has the ability to “drain the swamp” from within. And while the media fumbles over his unexpected rise, the rest of the world is watching in shock, horror and awe as the prospect of President Trump becomes achingly real. At the surface, it’s easy to condemn the media for falling prey to his shallow tactics; but deep down I’m relishing every bite of this amuse-bouche under the naive assumption that it won’t last.<\/p>\n
However, I’m not here to push my opinion. Instead, I want to uncover what the numbers tell us: who are the likely candidates for each party? Are the pundits’ claims backed by hard statistics? What insights can we achieve with voting data and a simple predictive model?\u00a0Let’s find out…<\/em><\/strong><\/p>\n With\u00a0the primaries\/caucuses of the four carve-out states just passed (Iowa, New Hampshire, South Carolina and Nevada), we have a rich set of county-level voting data<\/a>\u00a0for each of the candidates. Using US census records, we can associate each of these counties with demographic features (such as race, education, income and homeownership) which we assume are adequate predictors of voting outcome. Ultimately, the results of our election predictor are only as good as this assumption.<\/p>\n With that in mind, here’s a sample of what our data looks like:<\/p>\n [easytable style=”white-space:nowrap;font-size:13px;”] For each candidate we have the number and fraction of votes they received as well as\u00a0eight representative features per county:<\/p>\n A quick run through the data reveals some of the\u00a0correlations that our model will rely on. If we examine the counties where different candidates have won, Marco Rubio and Bernie Sanders attract higher income voters. Trump wins in\u00a0lower income counties\u00a0overall, but seems to appeal to a broader range of voters. [For those wondering how the chart works, the boxed area represents the interquartile range while the lines represent the min\/max range #GCSEmaths<\/a>].<\/em>The Data<\/h2>\n
\nstate,county,party,candidate,votes,fraction_votes,income,black,hispanic,bilingual,senior,college,homeownership,firms
\nIowa,Adair,Republican,Donald Trump,104,0.256,47892,0.4,1.7,1.1,22.1,16.3,77.1,0.098
\nIowa,Adams,Republican,Donald Trump,68,0.249,45871,0.4,1.1,1.2,22,13.7,78.5,0.108
\nIowa,Allamakee,Republican,Donald Trump,193,0.281,48831,1.4,5.7,8,21.3,14.9,79.5,0.114
\nIowa,Appanoose,Republican,Donald Trump,292,0.348,39208,0.7,1.5,2.3,21.4,18.3,72.5,0.11
\nIowa,Audubon,Republican,Donald Trump,99,0.265,48313,0.4,1.1,1.2,24.3,16.6,80.4,0.074
\nIowa,Benton,Republican,Donald Trump,410,0.251,56669,0.6,1.3,2,16.9,18.8,80.4,0.091
\n[\/easytable]<\/p>\n\n
Initial Findings<\/h2>\n
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