H5PythonAdvanced
Customer Churn Prediction
Keeping a customer is far cheaper than winning a new one, so predicting churn — who's about to leave — is one of the highest-value tasks in applied data science. This capstone runs the full ML workflow on a telecom-style churn problem, but with a crucial business twist most tutorials skip: the default 0.5 threshold is almost never right.
Notebook
Enrol to read the notebook and download it to run.
What this module covers
Keeping a customer is far cheaper than winning a new one, so predicting churn — who's about to leave — is one of the highest-value tasks in applied data science. This capstone runs the full ML workflow on a telecom-style churn problem, but with a crucial business twist most tutorials skip: the default 0.5 threshold is almost never right.
By the end of it
- Who churns, and what customer characteristics predict it?
- Can we build a well-calibrated, honestly-evaluated churn model?
- Given the economics, what probability threshold should trigger a retention offer?
- Which levers (contract type, tenure, charges) should the business pull?