Summary of Bank Customer Churn Rate Logistic Regression and Decison Tree

I did this project to expand my passion for predictive analytics and model building. Utilizing Python and some fun packages, I created two different models (Logistic Regression and Decision Tree) to determine customer churn rate at a bank. I wanted to make this code scalable to different banks to track their customer’s behavior. Using the data, I was able to determine why/what customer groups were more likely to exit the bank. This information helps the bank determine strategies to keep their customers there.

This project highlights more of my skills in creating powerful predictive models in Python. Check it out here!