Applications of Modern Survival Modeling with Python

Explore the applications of modern survival modeling using Python in this 44-minute conference talk. Gain insights into how survival models can be used to predict time-to-event scenarios across various domains, from churn prediction to hardware engineering. Learn the fundamental concepts of survival analysis, including censored data, duration matrices, and survival curves. Discover when to choose survival models over other methods and how to utilize popular Python tools like Lifelines, Scikit-survival, and Convoys. Understand the interpretation of model results for both prediction and decision-making purposes. Delve into machine learning perspectives on survival analysis and its growing importance in data science. Follow along as the speaker covers topics such as descriptive analysis using survival curves, causality testing, forecasting survival distributions, and predicting survival for specific subjects. Enhance your data science toolkit with this comprehensive overview of survival modeling techniques and their practical applications.