1.6 Lab Assignment

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.

Forecasting e-commerce orders with XGBoost

State Eviction Policy Models 2 for PAA poster

https://github.com/maRce10/rat_vocalization_alcohol