Optimal Strategies for Winning The Democratic Primaries

By: Dr. Ikjyot Singh Kohli Election season is upon us again, and a number of people from political analysts to campaign advisors are making a huge deal about winning the Iowa caucuses. This seems to be the standard “wisdom”. I decided to run some analysis on the data to see if it was true. I […]

Data Analytics Data Science Mathematics Politics Science Statistics

The Relationship Between The Electoral College and Popular Vote

An interesting machine learning problem: Can one figure out the relationship between the popular vote margin, voter turnout, and the percentage of electoral college votes a candidate wins? Going back to the election of John Quincy Adams, the raw data looks like this: Electoral College Party Popular vote  Margin (%) Turnout Percentage of EC John […]

Data Analytics Data Science Elections Mathematics Politics

2016 Michigan Primary Predictions

Using the Monte Carlo techniques I have described in earlier posts, I ran several simulations today to try to predict who will win the 2016 Michigan primaries. Here is what I found: For the Republican primaries, I predict: Trump: 89.64% chance of winning Cruz: 5.01% chance of winning Kasich: 3.29% chance of winning Rubio: 2.06% […]

Data Science Elections Politics Statistics

Hillary Clinton Still Has the Best Chance of Being The Democratic Party Nominee in 2016

A great deal of noise has been made in the previous weeks about the surge in the polls of Donald Trump and Bernie Sanders. This has led some people to question whether Hillary Clinton will actually end up being the Democratic party nominee in 2016. This was further evidenced by the fact that Sanders is […]