Survival Analysis of Lung Cancer Patients

This project focuces on analysis of survival patients with Aids, with Python library Lifelines

In this work I tested if there was an improvement in performance in the use of different survival models.

In this project I analyze data from a randomized clinical trial on the effectiveness of two treatments for smoking cessation.

Python survival-analysis engine for retention strategy testing: K-Means personas, CoxPH runway modeling, high-risk scenario simulation, Kaplan-Meier curves, and executive PPTX/PDF outputs.

The GBSG data set contains patient records of 720 patients with node positive breast cancer; it retains the 686 patients with complete data for the prognostic variables. Here, we have analyzed the survival time of these patients.