Deep Learning
Deep learning has the advantage of it doesn't need to be told what to do. It will scan and process the data and extract features that correlate and combine to enable faster learning without direction.
Deep Learning Algorithms
Utilize deep learning to derive patterns in voter behavior that cannot easily be found otherwise.
Develop Data for Model
Perhaps the most difficult part of deep learning is obtaining and cleaning data that can be used to project outcomes. The data has to be properly curated and maintained in order to have the deep learning methods pruduce usable results.
Train The Model
With any deep learning methods one uses part of the known data as a training set and part as a test set. In this manner, the deep learning method architecture and specifics can be tuned to give best results and make accurate forecasts for the campaign.
Exercise The Trained Model
Present updated and/or new data to the trained deep learning model and project outcomes needed for the political campaign strategy. Run various deep leaning models to reinforce the projected outcomes.
Choosing the Right Deep Learning Method
Utilize our collection of deep learning methods to get the best representation of voting behavior.
Neural Networks
We utilize deep neural networks to draw out relationships between voting precinct demographics and other factors. These relationships can be mapped to voting patterns and give guidance as to how to target specific voting precincts.
Clustering Algorithms
Clustering deep learning methods are additional ways in which voting distrcts can be understood. In this case, specific voting precincts can be "clustered together" mathematically and a single campaign strategy employed for those prceincts.
Graph Convolutional Networks
Graph convolutional neural networks are used to draw parallels between precincts in a political race. By drawing the geographic connections between precincts, like-minded precincts can e targeted with a similar campaign strategy.
Cellular Automata
We also utilize cellular automata methods that allow a voting population to "grow" or "shrink" within the district as the voting population and demographics change.