An exploratory data analysis project aimed at identifying bank customers at risk of attrition (those likely to terminate their service contracts). By understanding the underlying churn factors, the analysis enables the bank to adopt targeted strategies to reduce attrition and retain customers, using Python's data-science stack for cleaning, visualization, and statistical exploration.
Combines process mining and machine learning to detect fraudulent insurance claims from event-log data.