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