Intro to Survival Analysis in Python | Census
Blog post from Census
Survival analysis is a statistical technique that helps analyze the expected duration of time until an event occurs, providing valuable insights into customer and product lifecycle, employee churn, machine failure, and campaign effectiveness. It can be applied to various industries, including real estate, mortgage, manufacturing, and more, to predict key metrics such as active user survival rate, product time to purchase, campaign effectiveness evaluation, employee churn estimation, and machine lifecycle measurement. The Kaplan-Meier method is a commonly used approach in survival analysis, which involves computing survival probabilities from observed events while making assumptions about participants who dropped out and the event's occurrence at specified times. Python provides the lifelines library for implementing survival analysis, allowing users to harness its value in improving customer insights and operationalizing data.
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