lifelines

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.

C++17 console implementation of Who Wants to Be a Millionaire with validated CSV question banks, a prize ladder and three lifelines

Most housing risk models ask "which homes will fail?" This one asks "which homes are we dangerously confident about?" Separates genuine low-risk from low-risk-because-nobody-checked, using ensemble disagreement as an uncertainty signal, then optimises inspection and capital spend around what we actually don't know.

Predicts what happens to passengers after their flight is cancelled, where they go, when they'll actually resolve, and what it costs the airport. Classification vs. a transparent benchmark, survival analysis for timing, and a Passenger Half-Life metric that shows which disrupted groups keep generating pressure for hours.

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

Продуктовая аналитика удержания в EdTech: воронка, точки роста, модели оттока и анализ выживаемости. Выпускная квалификационная работа на данных реальной компании.

Applying KaplanMeierFitter model on Time and Events

Analyse de survie appliquée aux historiques Git pour comprendre pourquoi et quand les contributeurs abandonnent un projet.

Employee attrition prediction (XGBoost, 0.83 ROC-AUC) with SHAP explainability, Cox survival analysis, cohort retention, and a Streamlit dashboard — built on a responsible-use framework (protected attributes excluded, disparate-impact audit).

A comprehensive survival analysis study on clinical heart failure data, covering censoring principles, Kaplan-Meier curves, log-rank tests, multivariate Cox regression, and Random Survival Forests.top

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

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.

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

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

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 port of my Master's thesis — Cox, Random Survival Forest, and Gradient Boosting on time-to-invoice data

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