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Prediction of Voting Patterns Based on Census and Demographic Data

Prediction of Voting Patterns Based on Census and Demographic Data. Analysis Performed by: Mike He ECE 539, Fall 2005. Abstract. Prediction of Voting Patterns in 2004 Presidential Election Multi-Layer Perceptron, Back-Propagation Based on Demographic Data Population Size Gender Composition

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Prediction of Voting Patterns Based on Census and Demographic Data

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  1. Prediction of Voting Patterns Based on Census and Demographic Data Analysis Performed by: Mike He ECE 539, Fall 2005

  2. Abstract • Prediction of Voting Patterns in 2004 Presidential Election • Multi-Layer Perceptron, Back-Propagation • Based on Demographic Data • Population Size • Gender Composition • Racial Composition • Age Composition

  3. Voting Representations • Area-Based Winner- Takes-All Map • Strict Red/Blue binary color coding • Can misrepresent actual popular opinion • Population-Based Winner-Takes-All Cartogram • Counties resized to reflect actual population • More accurately reflects popular opinion • Illustrates high density of urban areas and tendency to vote Democratic • Linearly Shaded Vote-Percentage Map • Colors shaded according to vote percentages • Accurately portrays closeness of most races and political homogeneity throughout country

  4. Experimental Procedures • Data Pre-Processing • Network Structure Determination • # of Hidden Layers, Neurons in Layers • Coefficients Determination • Training, Training Error Testing • Error from vote percentages, calling for candidate • Testing on Testing Data Set

  5. Experimental Parameters • 14 Features, 3 Outputs • Hyperbolic Tangent Activation Function for Hidden Layers • Sigmoid Activation Function for Output Layer • Learning coefficient α=0.2 • Momentum coefficient μ=0.5

  6. Experiment 1 – Network Structure • Many different structures tested according to total square error • Best performers isolated for further testing • Comparison of error across multiple trials between tested structures • Winner: 15 neurons in hidden layer, 4 hidden layers

  7. Experiment 2 - Coefficients • To determine optimum α and μ • Different sets of coefficients tested based on total square error as well as maximum square error • Chosen configuration: • α = 0.2, and μ = 0.5

  8. Classification Results • Application of MLP to attempt to predict which candidate will win each county • 100 training and prediction trials • For Wisconsin (training data), 77% classification rate • For Minnesota (testing data), 75% classification rate • Less than 3% standard deviation in classification rate between trials

  9. Concluding Remarks • Impressive overall predictive power • Retains predictive power for different states: • Wisconsin and Minnesota similar demographically, different politically • Predictions based only on demographics – innocuous data leads to powerful results • Demonstrates effectiveness of MLP’s as well as element of truth in common generalizations of demographic voting tendencies

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