Survival Analysis in Health Data

A small, well-documented survival analysis project demonstrating both
classical biostatistics (Cox PH) and machine learning (Random Survival Forest)
for time-to-event outcomes in healthcare-style datasets.

What this project shows

• Clean project structure (data / notebooks)
• Survival modeling workflow (EDA → Modeling → Evaluation)
• Ethical handling of health data (no raw patient-level data shared)

Methods

• Kaplan–Meier exploration (EDA notebook)
• Cox Proportional Hazards (interpretable hazard ratios)
• Random Survival Forest (non-linear ML survival model)

Evaluation (planned / included as data allows)

• Concordance Index (C-index)
• Risk stratification and survival curves
• Calibration (optional)

Repository structure

Survival-analysis-in-health-data/
├── data/        # Data description and access instructions (no raw data uploaded)
├── notebooks/   # 01_eda.ipynb, 02_modeling.ipynb (+ evaluation later)
├── README.md
└── .gitignore