Using lifelines to replicate published articles

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

DecenniumClinic is a reproducible Python stack for epidemiology-style cohorts: validation → imputation-in-pipeline random forests → Harrell’s C tuning, plus /health, /ready, and curl-friendly APIs. Built for methods research, not patient care.

Applying KaplanMeierFitter model on Time and Events

end-to-end survival analysis on 15,054 breast cancer patients using Kaplan–Meier, Cox PH, and Weibull AFT models to identify prognostic factors and evaluate survival outcomes

Cox survival validation of 8 DNA-methylation clocks against ~20-year NHANES mortality follow-up (n=2,532)

A biostatistical survival analysis pipeline using Python to evaluate patient prognosis in the Mayo Clinic PBC dataset. Implements Kaplan-Meier estimators and Cox Proportional Hazards models to mathematically process right-censored clinical data and identify mortality risk factors.

This is a interactive math quiz game in which player has to complete a given math equation that can be of multiplication subtraction or addition in a given time

Survival prediction model on TCGA-BRCA data · Python · Lifelines · Streamlit

Survival analysis of breast cancer clinical data using Kaplan–Meier curves and Cox proportional hazards models in Python

Notebooks for "A topic model analysis of TCGA transcriptomic data of breast and lung cancer"

Pancreatic Cancer Predictive Pipeline A professional clinical framework for pancreatic cancer prognosis. Combines Kaplan-Meier survival analysis and Cox Regression with an MLOps-powered machine learning pipeline (XGBoost/Random Forest) for real-time, high-recall patient risk stratification.

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.

Project and tutorial for analyzing datasets with Python, pandas, lifelines, matplotlib, statsmodels, and seaborn

A repository containing various projects and microprojects.

Analyzed user events from a leading scheduling SaaS platform to uncover what drives activation, engagement, and subscription [Part 2]

How long does a track & field world record stand? A survival analysis (Kaplan-Meier + Cox) of ~250 record reigns — and which of today's records is most likely to fall next.

This repository explores the integration of Machine Learning with the Weibull Distribution to improve the accuracy of Remaining Useful Life (RUL) estimations. By treating the Weibull scale parameter ( λ ) as a dynamic target for regression, these projects bridge the gap between statistical reliability analysis and modern data-driven maintenance.