Heart_Disease_ML_Implementation

This repository consists of a Jupyter notebook which implements various machine learning models on a dataset of patients to detect their likelihood of heart failure. The dataset comes from a Kaggle posting, and the original data is from Davide Chicco and Giuseppe Jurman (https://www.kaggle.com/andrewmvd/heart-failure-clinical-data). The notebook examines the data, chooses features for learning, implements various machine learning models using the sklearn library, and compares the performance of different models. The results indicate that a Random Forest model has the best performance, but also has the largest gap between training and test performance, indicating overfitting.