1. Predicting Voter Choice from Census Data
Isaac Evavold

2. Motivation
Election models built by hand using vote history, polling data
Interested in viability of predictions made using census data and neural network
Build a network that could make voting predictions about arbitrary groups of voters given aggregate statistics about group

3. Data Data required a lot of pruning and consolidation
Collected from 2010 US Census
Aggregated per state and per congressional district
Pro: Approximately same population nationwide
Con: Produces a limited dataset to train and evaluate model
Data required a lot of pruning and consolidation

4. Methods Feature vector includes:
District population
Racial composition
Median income
Education level
Each district was classified by whether it voted for the Democratic or Republican presidential candidate in 2016
Neural network used 4 hidden layers with neurons

5. Results & Conclusions
Network was able to make correctly predict vote ~64% of the time
More experiments with different feature vectors or more sophisticated data could yield better predictions
This approach is still limited by using census data which cannot account for current events, only demographic voting trends