https://github.com/thisisashukla/survival-analysis

Hands-On Survival Analysis in Python
https://github.com/thisisashukla/survival-analysis

data-analysis data-science survival-analysis

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Hands-On Survival Analysis in Python

• Host: GitHub
• URL: https://github.com/thisisashukla/survival-analysis
• Owner: thisisashukla
• Created: 2019-12-01T17:31:11.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-28T22:19:43.000Z (almost 5 years ago)
• Last Synced: 2024-11-07T16:48:15.595Z (3 months ago)
• Topics: data-analysis, data-science, survival-analysis
• Language: Jupyter Notebook
• Size: 2.47 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Survival Analytics

## About the Content

Survival analysis was initially used by medical practitioners and actuaries to deal with problems such as:

- What is life expectancy of patients in cohort A as compared to cohort B (where one of the cohorts would be given a drug under study)?

- What is the life expectancy of the population in Gurgaon?

However these methods/models are now used for variety of tasks such as to determine machine failure time, customer churn duration.

These models are different from the regression model in the following way:

- Survival analysis is specially designed to handle data censorship

- In survival analysis we aim to derive the survival/hazard functions unlike lifespan values for individual samples as in a regression setup.

## Objectives

By the end of the tutorial series you will be able to understand the motivation behind using survival analysis models. You will also be able to appreciate the ideas of using parametric and non-parametric models for the same. This tutorial also presents code to make parametric estimations from scratch and verify them with Lifelines package.

## Software Pre-requisites

You will need a Python environment with Numpy, Scipy, Matplotlib, Seaborn, Lifelines installed

## Knowledge Pre-requisites

Working knowledge of Python and Jupyter notebooks is essential for this course along with concpetual understandin of concepts of probability

## Tutorial Outline

- [0. Motivation](https://github.com/thisisashukla/survival-analysis/blob/master/0.%20Motivation.ipynb)

- [1. Survival  with Constant Hazard Function](https://github.com/thisisashukla/survival-analysis/blob/master/1.%20Survival%20with%20Constant%20Hazard%20Function.ipynb)

- [2. Non-Parametric Kaplan-Meier Model](https://github.com/thisisashukla/survival-analysis/blob/master/2.%20Non-Parametric%20Kaplan-Meier%20Model.ipynb)

- [Parametric Models Weibull Curve Estimation](https://github.com/thisisashukla/survival-analysis/blob/master/3.%20Parametric%20Models%20Weibull%20Curve%20Estimation.ipynb)

- [Course Deck](https://github.com/thisisashukla/survival-analysis/blob/master/Survival%20Analytics.pptx)

## About the Author

Ankur Shukla is a Data Science Analyst at Deloitte Consulting. He consult clients from different industries on their data science problems. Python is his bread and butter and he uses it extensively for his day to day machine learning and data analysis tasks. Ankur is a postgraduate from CSRE, IIT Bombay in Geoinformatics and Natural Resources Engineering. Majority of his work at CSRE was focused in satellite image processing using Python.

[LinkedIn](https://www.linkedin.com/in/work-ankur-shukla/)