- A Complete Guide To Survival Analysis In Python, part 3 - Jul 30, 2020.
Concluding this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter based on different groups, a Log-Rank test, and Cox Regression, all with examples and shared code.
Jupyter, Python, Regression, Statistics, Survival Analysis
- A Complete Guide To Survival Analysis In Python, part 2 - Jul 14, 2020.
Continuing with the second of this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter theory as well as the Nelson-Aalen fitter theory, both with examples and shared code.
Python, Statistics, Survival Analysis
A Complete Guide To Survival Analysis In Python, part 1 - Jul 7, 2020.
This three-part series covers a review with step-by-step explanations and code for how to perform statistical survival analysis used to investigate the time some event takes to occur, such as patient survival during the COVID-19 pandemic, the time to failure of engineering products, or even the time to closing a sale after an initial customer contact.
Python, Statistics, Survival Analysis
- Survival Analysis for Business Analytics - Nov 27, 2017.
We compare survival analysis to other predictive techniques, and provide examples of how it can produce business value, with a focus on Kaplan-Meier and Cox Regression methods which have been underutilized in business analytics.
Business Analytics, Survival Analysis, Time Series
- The Challenges of Building a Predictive Churn Model - Mar 8, 2017.
Unlike other data science problems, there is no one method for predicting which customers are likely to churn in the next month. Here we review the most popular approaches.
Churn, Customer Analytics, Datascience.com, Survival Analysis
