Comprehensive Machine Learning Full Pipeline on Heart Disease UCI Dataset

This project aims to analyze, predict, and visualize heart disease risks using machine learning. The workflow involves data preprocessing, feature selection, dimensionality reduction (PCA), model training, evaluation, and deployment. Classification models like Logistic Regression, Decision Trees, Random Forest, and SVM is used, alongside K-Means and Hierarchical Clustering for unsupervised learning. Additionally, a Streamlit UI is built for user interaction, deployed via Ngrok, and the project is hosted on GitHub.