Loading...

Linda Uruchurtu - Survival Analysis in Python and R

4,790 views

Loading...

Loading...

Transcript

The interactive transcript could not be loaded.

Loading...

Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on May 11, 2016

PyData London 2016

Survival analysis is a set of statistical techniques that has many applications in the industry. This talk will discuss key concepts behind survival analysis by means of examples implemented via Lifelines, an open source python library, and in R for comparison purposes. I will also describe how we have made use of these techniques in Lyst to try to predict when items go out of stock.

Many problems involve the understanding the duration of specific events; for example, predicting when a customer will churn, when a person will default on a credit, how long a machine will work, etc. These type of questions constitute the realm of Survival analysis, a branch of statistics historically developed by professionals in the actuarial and medical fields dealing with event durations as governed by probability laws.

In this talk I will cover the basics of Survival analysis via examples implemented via Lifelines, an open-source python library and in R (survival and KMsurv libraries), going from survival curves to regression models. I will discuss how survival analysis can be applied to a variety of problems and in particular, I will focus on the problem of out of stock prediction for an online retailer.

Slides available here: http://bit.ly/1Oe1gf7

Comments are disabled for this video.
When autoplay is enabled, a suggested video will automatically play next.

Up next


to add this to Watch Later

Add to

Loading playlists...