KaterynaD / 2016-US-President-Election-Primary-Results-Analysis

Correlation analysis between candidates and county facts based on 2016 US President Election Primary Results by county

Data Source: Kaggle 2016 US Presidential Election dataset

1. primary_results_candidates_correlation.py explores the correlations between candidates based on votes fractions they have in the same counties. The results are saved in CandidateCorrelation folder
   1. Most two anti-correlated Democrat party candidates are Hillary Clinton and Bernie Sanders. The script calculates Pearson correlation coeficient, PValue and StdErr

      Here is the Pvalue and StdErr

      Here is the joinplot of these two candidates:

      

   2. The more interesting question is anti-correlated Repupublican candidates. According to my analysis they are:
      • Donald Trump and Marco Rubio
      • Marco Rubio and Ted Cruz

      Both pairs have -0.49 Rvalue. The next pair is Marco Rubio and Mike Huckabee with -0.42 Rvalue The data has strong negative correlation, and it's significant as p-value is a lot lesser than 0.001

      

      Here is the Pvalue and StdErr

      Here are the joinplots of two first pairs:

      • 
      • 
   3. Primary results assume a choice between Democrats candidates only or Republican candidates only So comparing Democrats to Republicans based on these results does not have a lot of sense However let's look on the picture as a whole

      

      or in this view

      Let's look now how high is the PValue for correlations between democrat and republican candidates We can not trust such results

   4. And at the end the pairplot for the data set:

      

2. county_facts_candidates_correlation.py explores the correlations between candidates and county facts based on votes fractions they have in each county. The results are saved in FactCandidateCorrelation folder
   1. There is a strong correlation between percent of Asian and Bernie Sanders votes fraction. In the opposite, Hillary Clinton has anti-correlation with Asian percent and stong positive correlation with White percent.

      

      The PValue is small enough to trust the results

   2. Here is the similar analysis for republicans. the results are more sparse but what we can see the strong positive relationship between percent of Housing units in multi-unit structures and votes fractions of John Kasich, Marco Rubio and Rand Paul.

      There is also the strong correlation between percent of Bachelor's degree or higher and the same republican candidates

      The PValue is very low and we can trust the results.

      Interesting, Donald Trump has the strong anti-correlated results with the percent of Bachelor's degree or higher Fact with a low PValue

      He has a moderate positive correlation with the percent of Persons 65 years and over. However the PValue is high in this case

      Marco Rubio fraction votes is strongly anti-correlated with the percent of Persons 65 years and over fact and PValue is very low.

      

   3. Here is the full picture: RValue and Pvalue The fact dictionary is here.
3. LinearRegression.py predicts primary results fraction votes based on demographic county facts. Hillary Clinton and Bernie Sanders fraction votes are most correlated to the county facts. The variance is above 0.6 for these 2 candidates. The quality of the predicted values for the rest of the candidates is low with 0.4 and less varience values.

   Ordinary least squares method works perfectly fine fo the data. The rest of the method can give a slightly better results but not very significant

   Hillary Clinton fraction votes prediction residual plot for ordinary least squares method, not normalize data

   

   Hillary Clinton prediction joint plot for ordinary least squares method, not normalize data

   

   The files with the predicted data and plots for each candidates can be found in LinearRegressionPredictionPrimary folder

   Other candidates prediction data models fit for different methods and parameters. More data can be found here
   

   candidate	method	normalize	MSE Train set	MSE Test set	Variance
   Hillary Clinton	LeastSquares	Y	0.011	0.011	0.614
   Hillary Clinton	LeastSquares	N	0.011	0.011	0.614
   Hillary Clinton	Ridge 0.010	Y	0.011	0.011	0.616
   Hillary Clinton	Ridge 0.010	N	0.011	0.011	0.614
   Hillary Clinton	Lasso 0.000	Y	0.012	0.011	0.627
   Hillary Clinton	Lasso 0.000	N	0.011	0.011	0.618
   Hillary Clinton	BayesianRidge	Y	0.011	0.011	0.620
   Hillary Clinton	BayesianRidge	N	0.011	0.011	0.610
   Bernie Sanders	LeastSquares	Y	0.010	0.010	0.642
   Bernie Sanders	LeastSquares	N	0.010	0.010	0.642
   Bernie Sanders	Ridge 0.010	Y	0.010	0.010	0.643
   Bernie Sanders	Ridge 0.010	N	0.010	0.010	0.642
   Bernie Sanders	Lasso 0.000	Y	0.011	0.010	0.649
   Bernie Sanders	Lasso 0.000	N	0.010	0.010	0.646
   Bernie Sanders	BayesianRidge	Y	0.010	0.010	0.643
   Bernie Sanders	BayesianRidge	N	0.010	0.010	0.640
   Donald Trump	LeastSquares	Y	0.005	0.006	0.401
   Donald Trump	LeastSquares	N	0.005	0.006	0.401
   Donald Trump	Ridge 0.010	Y	0.005	0.006	0.426
   Donald Trump	Ridge 0.010	N	0.005	0.006	0.402
   Donald Trump	Lasso 0.000	Y	0.005	0.006	0.417
   Donald Trump	Lasso 0.000	N	0.005	0.006	0.407
   Donald Trump	BayesianRidge	Y	0.005	0.006	0.428
   Donald Trump	BayesianRidge	N	0.005	0.006	0.411
   Marco Rubio	LeastSquares	Y	0.004	0.004	0.228
   Marco Rubio	LeastSquares	N	0.004	0.004	0.228
   Marco Rubio	Ridge 0.010	Y	0.004	0.004	0.242
   Marco Rubio	Ridge 0.010	N	0.004	0.004	0.228
   Marco Rubio	Lasso 0.000	Y	0.004	0.005	0.226
   Marco Rubio	Lasso 0.000	N	0.004	0.004	0.242
   Marco Rubio	BayesianRidge	Y	0.004	0.004	0.253
   Marco Rubio	BayesianRidge	N	0.004	0.004	0.243
   Ted Cruz	LeastSquares	Y	0.009	0.008	0.326
   Ted Cruz	LeastSquares	N	0.009	0.008	0.326
   Ted Cruz	Ridge 0.010	Y	0.009	0.008	0.348
   Ted Cruz	Ridge 0.010	N	0.009	0.008	0.326
   Ted Cruz	Lasso 0.000	Y	0.009	0.008	0.374
   Ted Cruz	Lasso 0.000	N	0.009	0.008	0.322
   Ted Cruz	BayesianRidge	Y	0.009	0.008	0.362
   Ted Cruz	BayesianRidge	N	0.009	0.008	0.318

4. The rest of scripts were used to generate data for Tableau