A reproducible machine learning pipeline for breast cancer risk prediction using logistic regression, featuring stratified train-test splitting, standardization, and evaluation via confusion matrix and classification metrics.

Flask–React web app for breast cancer risk prediction using logistic regression.

A modern breast cancer risk prediction dashboard built with FastAPI and React. Features real-time ML predictions using Logistic Regression, interactive UI with prediction history, and a RESTful API. For educational purposes only.

My Project on Breast Cancer Prediction using a Logistic Regression Model. In this project, I developed a predictive model to assist in breast cancer risk based on various input features. This work has strengthened my understanding of machine learning and data analysis techniques. Feel free to explore the code and insights!

This project predicts diabetes, breast cancer, and heart disease using machine learning algorithms on medical datasets. It applies models like Logistic Regression and Random Forest to support early diagnosis and risk prediction based on patient health data.

Healthcare Data Scientist | 3 Portfolio Projects: Breast Cancer SVM (98% Recall), Heart Disease Risk (Logistic Regression), & Hospital No-Show Prediction (Random Forest). Specialized in feature engineering, SQL database normalization, and solving technical blockers. Focused on clinical safety and business efficiency

Healthcare Data Scientist | 3 Portfolio Projects: Breast Cancer SVM (98% Recall), Heart Disease Risk (Logistic Regression), & Hospital No-Show Prediction (Random Forest). Specialized in feature engineering, multicollinearity removal, and solving technical blockers like URLError simulation. Focused on clinical safety and business efficiency

Built a breast cancer survival prediction model using the METABRIC dataset (2,509 patients, 34 features). Applied EDA, data preprocessing, and trained 3 ML models (Logistic Regression, Decision Tree, Random Forest) with GridSearchCV tuning. Achieved ~98% recall with Random Forest — prioritising clinical sensitivity for high-risk patient detection