Predicting and Analyzing Heart Disease with Machine Learning

This project is the capstone assignment for the spring 2019 Data Science course hosted by LaunchCode. It aims to model and predict the presence of heart disease in patients by using binary classification machine learning algorithms (random forest, k-nearest neighbors, logistic regression, Naive Bayes) and a dataset of patient information, which can be found on Kaggle and UC Irvine's Machine Learning Repository.

The project is divided into three separate Jupyter notebooks:

1. Exploratory Data Analysis
2. Modeling
3. Conclusions