Towards Data Science
A Survival Analysis Guide with Python: Using Time-To-Event Models to Forecast Customer Lifetime
β’1 min readβ’
#python#deployment#rag
Level:Intermediate
For:Data Scientists, ML Engineers
β¦TL;DR
This article provides a comprehensive guide to survival analysis using Python, focusing on time-to-event models to forecast customer lifetime and retention. By utilizing Kaplan-Meier curves and Cox Proportional Hazard regressions, data scientists can gain valuable insights into customer behavior and develop more effective retention strategies.
β‘ Key Takeaways
- Survival analysis is a statistical technique used to analyze the time it takes for a specific event to occur, such as customer churn.
- Kaplan-Meier curves are a type of survival curve that can be used to visualize and estimate the survival function of a population.
- Cox Proportional Hazard regressions are a type of regression model that can be used to identify the factors that affect the likelihood of an event occurring.
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