Hails from the mountains. Mapslinging and data plumbing with faraday.io.
Google and Microsoft both allow for flexible geographic targeting up to a point, which means we can use AI to bundle groups of individuals, find the commonalities, and make a recommendation about how much a marketer should be willing to spend to engage with them.
While there are various types of propensity models, the one we use most at Faraday is the random decision forest. Like real forests, this one is made of trees — decision trees. Decision trees are classifier algorithms that look like flow charts, showing the choices made to reach a certain outcome.
A fundamental part of location intelligence, spatial aggregation combines individuals into groups and generalizes information about them.
An algorithm contains the biases of its builder. At Faraday, we have a handful of approaches we use to minimize these effects at each level of our machine learning pipeline.
Most data scientists practice some version of what could be called a data survey; the goal is to surface meaningful patterns, gaps, and anomalies with an eye toward prediction.
This post is part of our data science series. The U.S. Census and American Community Survey (ACS) are the crown jewels of open data (bother your Representative today to make sure they stay that way), but working with data from the Census API isn't always intuitive. Here's an example