Michelle Zheng

High employee turnover poses significant financial and organizational challenges across industries. This practicum project applies an end-to-end data science approach to predict employee attrition using supervised machine learning models. The project encompasses data acquisition, preprocessing, exploratory analysis, model development, and evaluation using Python. Multiple algorithms are compared to identify the most effective predictive model and to uncover key drivers of attrition such as job satisfaction, compensation, and work-life balance.

Recidivism is the tendency of formerly incarcerated individuals to reoffend. This practicum aims to develop predictive models of recidivism and to characterize post-release reentry trajectories. The projected practicum includes the acquisition and cleaning of government based criminal records, exploratory data analysis, feature engineering of risk factors, and the construction and comparison of multiple predictive models. By integrating advanced predictive modeling, this project aims to contribute further insight to the criminology-data science interface and actionable insights to reduce reoffending.