Intro to Survival Analysis
About This Video
Survival analysis is a technique in statistics to deal with time to an event. Some problems that this technique can help with are: “What is the likelihood of needing maintenance at x years?” “What is the probability of surviving after having cancer for x years?”. We will explore different applications of survival analysis, important concepts and different modeling techniques in Python. At Peloton, we use this for finding the likelihood of an event after usage (miles ridden on bike). We understand that members who use their bikes more frequently will have a different experience than others. Through these techniques we can make predictions that allow us to meet our members where they are in their product journey.
In This Video
Data Scientist, Peloton
Diana is a Senior Data Scientist at Peloton working on creating value for the business from the product lifecycle. Diana has her Bachelors in Mathematics and Masters in Operations Research from Northeastern University.