Comprehensive breast cancer data analysis on 4,024 patient records. Performs exploratory analysis, statistical testing, machine learning classification, clustering, and survival prediction using Python, Scikit-learn, and ensemble methods.

This project applies statistical and machine learning techniques to analyze breast cancer patient data, perform survival analysis, and predict 10-year mortality risk. It combines classic survival modeling with modern classification approaches to uncover key prognostic factors influencing long-term outcomes.

Built a breast cancer risk prediction system using Python, Scikit-learn, and Streamlit. Developed a complete ML pipeline with data preprocessing, hyperparameter tuning (Random Forest + GridSearchCV), and evaluation using classification metrics and survival analysis. Deployed via a user-friendly Streamlit app for real-time risk prediction.