  Employee theft and dishonesty is a major contributor to loss in the retail
industry. Retailers have reported the need for more automated analytic tools to
assess the liability of their employees. In this work, we train and optimize
several machine learning models for regression prediction and analysis on this
data, which will help retailers identify and manage risky employees. Since the
data we use is very high dimensional, we use feature selection techniques to
identify the most contributing factors to an employee's assessed risk. We also
use dimension reduction and data embedding techniques to present this dataset
in a easy to interpret format.
