Applying Data Science and Mathematical Modeling to Predict Election Verdict Exit polls, or combinations of exit polls, have been traditionally used to predict the results of elections. In the absence of exit polls, an interesting question is whether the results of an election can be predicted through mathematical models using publicly available data, including social media sources such as Twitter. Conducting exit polls has its own associated logistical challenges and most importantly, it is conducted after voters have already casted their vote and hence cannot be of much use prior to elections. In the2013 Kenya election, ODINGA’s campaign made heavy use of data sciences to understand people’s opinion and its temporal change closely, so that their campaigns could be optimized. Now, our attempt is to make use of similar predictive models in the JKUAT context, to predict election outcomes in advance, leveraging our expertise in data sciences and mathematical modeling. The outcome of election depends on the outcome of the election in each DEPARTMENT or territory) which in turn depends on the individual outcome in each course, it may be appropriate to consider the question of predicting the election outcome at the level of the course. The election outcome for a course is the aggregated preference of the voters in that course, hence, a possible method of predicting the election outcome is to estimate the voting preference through statistical models, making use of historic data (previous election outcomes) and changing user preferences over time, expressed through opinion polls and social media. We will be analyzing and updating u
Posted on: Tue, 02 Sep 2014 16:17:16 +0000
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