This project aims to predict heart disease using machine learning models based on key cardiovascular health indicators. The dataset, sourced from Kaggle, includes 11 features related to patient health. Various classification models, including Logistic Regression, Decision Tree, Random Forest, and Neural Networks, were trained and evaluated. The Random Forest model achieved the highest accuracy (85.5%). To further improve performance, an ensemble model combining Logistic Regression, Random Forest, and Neural Networks was implemented, increasing accuracy to 89.6%. The project highlights the effectiveness of machine learning in medical diagnosis and the impact of ensemble learning on predictive performance.

This project analyzes the effectiveness of different Trash Wheels in reducing waste in the Baltimore Harbor over the years, exploring the relationship between precipitation levels and the amount of trash collected to assess the impact of weather on urban water pollution management.

"The Rhythm of Bob Marley: A Data-Driven Exploration" analyzes Bob Marley's Spotify audio features, uncovering patterns in danceability and valence to understand the unique musical characteristics that define his legendary sound.