Skip to content

Latest commit

 

History

History
35 lines (20 loc) · 1.32 KB

File metadata and controls

35 lines (20 loc) · 1.32 KB

Survival Analysis: Intuition & Implementation in Python

Quick Implementation in python

There is a statistical technique which can answer business questions as follows:

  • How long will a particular customer remain with your business? In other words, after how much time this customer will churn?
  • How long will this machine last, after successfully running for a year ?
  • What is the relative retention rate of different marketing channels?
  • What is the likelihood that a patient will survive, after being diagnosed?

If you find any of the above questions (or even the questions remotely related to them) interesting then read on. The purpose of this article is to build an intuition, so that we can apply this technique in different business settings.

Link to the article

Survival Analysis

Table of Contents

1. Introduction
2. Definitions
3. Mathematical Intuition
4. Kaplan-Meier Estimate
5. Cox Proportional Hazard Model
6. End Note
7. Additional Resources