Project 1

"Heart Disease Prediction using Machine Learning"

In the "Heart Disease Prediction using Machine Learning" project, we analyze thirteen clinical parameters to predict the presence of heart disease in patients. We employ five machine learning algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest. The project aims to determine the most accurate model for early heart disease diagnosis and provides insights into critical clinical indicators.