Heart Disease Prediction & Classification Analysis

Developed a binary classification model to predict the presence of heart disease using structured clinical data. The project included exploratory analysis, class distribution assessment, supervised model training, and evaluation using accuracy and ROC–AUC metrics. Emphasis was placed on interpretability and diagnostic reliability, making the solution suitable as a baseline clinical decision-support model. The project demonstrates how machine learning can enhance early detection and healthcare analytics.