Member Churn Prediction for Sports Big Name
Analzing different factors’ effects to a customer’s churn.
Project Summary
- Identified UA customers with a high likelihood of churning within six months based on their purchasing behavior in the company’s marketing dataset.
- Built a logistic regression model using R to identify key factors contributing to churn rate, achieving an AUC score of 0.88.