Turning churn predictions into business decisions.

01 / The problem

I analyzed 10,000 retail-banking customers with a churn rate of roughly 20%. The project connected model performance to specific retention actions instead of stopping at a ranked model table.

The business needed enough recall to find at-risk customers, but low precision would waste outreach. No single metric could decide that trade-off without the campaign context.

02 / The approach

Build the evaluation around the decision.

1. 01

   Explored churn patterns across age, geography, balance, activity, and product use.

2. 02

   Compared six classification models with five-fold cross-validation.

3. 03

   Created behavioural features such as product usage, engagement, and balance-to-salary ratio.

4. 04

   Used PyCaret as a second check on the manually built pipeline.

03 / The result

Age, number of products, activity, and balance were the strongest signals. The practical output was a set of targeted retention ideas and a plan for ranking customers by intervention priority.