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Lavneet Sidhu | Nikita Bali | Sanjita Jain | Subhasree Chatterjee
29
states
1928
counties
33
Demographic
variables
Primary Results County Facts
1542
Training set counties
80 percent
386
Testing set counties
20 percent
Population %, Female %,
Different ethinicity %
Educational background, income,
Number of votes
Distribution of votes between
Democrats
as per demographic information
The explanatory data analysis was done using python.
Correlation Matrix:
1. The highest correlation is between the
population percentage where language other
than English is spoken at home and
Population that is either Hispanic or Latino.
2. There is also a very high correlation in
population that is not born in the US and
population where language other than
English is spoken at home and also with
population that is either Latino or Hispanic.
% African American
vs Fraction of votes
% non English speaking
vs. Fraction of votes
% of females
vs.
Fraction votes
% over 65 vs.
Fraction votes
Clinton 0
Sander
s
1Winner
Full Model Step AIC Step BIC
AIC 1086.87 1068.88 1076.96
BIC 1268.46 1181.05 1157.08
AUC (training) 0.918 0.917 0.913
AUC (testing) 0.794 0.760 0.771
Model
Selectio
n
Response
Variable
Variable
Selectio
n
Age, % females, % whites, % Afro-American, % native
Indians, % Hispanic Latino, % foreign born, % education,
% veterans, home ownership rate, median value of
house, person per household, per capita income, % of
Asian owned firms etc.
Training ROC Testing ROC
Clinton Sander
s
Clinton 920 131
Sander
s
124 367
Misclassification Rate:
0.165
Clinton Sander
s
Clinton 226 36
Sander
s
35 89
Misclassification Rate:
0.184
Classificatio
n Type
100
0
Trees
5
Variables
tried at each
split
17.3
%
OOB estimate
of error rateClinton Sande
r
Class.Erro
r
Clinton 1169 136 0.104
Sander
s
197 426 0.316
Confusion
Matrix
Importanc
e
Clinton Sander
s
Clinton 1129 176
Importance of
components
ROC
Testing
AUC = 86%
Testing
Confusion Matrix
Testing
Error = 22%
Testing
Washingto
n
39 counties
Hawaii
5 counties
Alaska
29 counties
0 39
C S
5 34
C S
0 5
Logistic Model
2 3
2 27
Actual Predicte
dRandom Forest
4 35
C S
0 5
1 28
PCA
Regression
2 37
C S
0 5
0 290 29
State Count
y
All states seem to exhibit similar
behavior except Hawaii, Alaska
& District of Columbia
All counties seem to exhibit
similar behavior
Clinton gets majority of the votes from the counties where median income is higher and non-
native and Hispanic Americans are more.
New York
62 counties 13 49
C S
25 37
C S
Logistic Model
Actual Predicte
dRandom Forest
27 35
C S
PCA Regression
6 56
C S
2016 Democrat Primary: Prediction of results For New York Counties
2016 Democrat Primary: Prediction of results For New York Counties

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2016 Democrat Primary: Prediction of results For New York Counties

  • 1. Lavneet Sidhu | Nikita Bali | Sanjita Jain | Subhasree Chatterjee
  • 2.
  • 3. 29 states 1928 counties 33 Demographic variables Primary Results County Facts 1542 Training set counties 80 percent 386 Testing set counties 20 percent Population %, Female %, Different ethinicity % Educational background, income, Number of votes
  • 4. Distribution of votes between Democrats as per demographic information The explanatory data analysis was done using python. Correlation Matrix: 1. The highest correlation is between the population percentage where language other than English is spoken at home and Population that is either Hispanic or Latino. 2. There is also a very high correlation in population that is not born in the US and population where language other than English is spoken at home and also with population that is either Latino or Hispanic.
  • 5. % African American vs Fraction of votes % non English speaking vs. Fraction of votes % of females vs. Fraction votes % over 65 vs. Fraction votes
  • 6. Clinton 0 Sander s 1Winner Full Model Step AIC Step BIC AIC 1086.87 1068.88 1076.96 BIC 1268.46 1181.05 1157.08 AUC (training) 0.918 0.917 0.913 AUC (testing) 0.794 0.760 0.771 Model Selectio n Response Variable Variable Selectio n Age, % females, % whites, % Afro-American, % native Indians, % Hispanic Latino, % foreign born, % education, % veterans, home ownership rate, median value of house, person per household, per capita income, % of Asian owned firms etc.
  • 7. Training ROC Testing ROC Clinton Sander s Clinton 920 131 Sander s 124 367 Misclassification Rate: 0.165 Clinton Sander s Clinton 226 36 Sander s 35 89 Misclassification Rate: 0.184
  • 8. Classificatio n Type 100 0 Trees 5 Variables tried at each split 17.3 % OOB estimate of error rateClinton Sande r Class.Erro r Clinton 1169 136 0.104 Sander s 197 426 0.316 Confusion Matrix Importanc e
  • 9. Clinton Sander s Clinton 1129 176 Importance of components ROC Testing AUC = 86% Testing Confusion Matrix Testing Error = 22% Testing
  • 10. Washingto n 39 counties Hawaii 5 counties Alaska 29 counties 0 39 C S 5 34 C S 0 5 Logistic Model 2 3 2 27 Actual Predicte dRandom Forest 4 35 C S 0 5 1 28 PCA Regression 2 37 C S 0 5 0 290 29
  • 11.
  • 12. State Count y All states seem to exhibit similar behavior except Hawaii, Alaska & District of Columbia All counties seem to exhibit similar behavior
  • 13. Clinton gets majority of the votes from the counties where median income is higher and non- native and Hispanic Americans are more.
  • 14. New York 62 counties 13 49 C S 25 37 C S Logistic Model Actual Predicte dRandom Forest 27 35 C S PCA Regression 6 56 C S

Editor's Notes

  1. Testing