Sometimes, the biggest challenge is creating a uniform dataset. Here we outline one recent example.
Difference-in-Difference
The Difference-in-Difference (DiD) analysis is a powerful econometric tool to analyze the difference between two groups after some type of treatment.
K-Nearest Neighbors, Part I
Using actual data from the Seattle Police Dept, we created a machine learning model to predict the likelihood of arrest after routine police stops.
K-Nearest Neighbors, Part II
The Machine Learning portion of the K-Nearest Neighbors exercise.