Churn in SaaS and readmission in healthcare can both be framed as time-to-event problems, so the same survival methods often apply. Same maths, different vocabulary, different traps.
Analyses the pipeline built from a real question in this field: each one became a module its owner can re-run.
Binary classification model to predict customer churn using demographic, service usage, and billing features. Identifies high-risk customers…
Analyze customer lifetime value (LTV) distribution, identify high-value vs at-risk customers, and understand what subscription patterns and…
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