Code related to practicum project as part of Master of Science in Business Analytics program at Wake Forest University.
Our task was to generate a classification model that could help predict member churn for a credit union.
- Data ETL performed with Python's Pandas
- R used to generate survival curves, classification trees, logistic regression models
- Final model was an ensemble of predictions from logistic regression and classification tree models