Decision_Tree_Classifier_Heart_Disease

I implemented an end-to-end machine learning model utilizing decision trees and random forests to predict heart disease due to a variety of environmental and biologic factors. In this project I really delved under the hood to better understand the hyperparameter tuning of each model. One large difficulty in creating this model was that the dataset was extremely imbalanced.

Questions:

1. Which factors contribute most to an individual being at risk for coronary heart disease (CHD)?
2. How can an imbalanced dataset be mitigated?

Dataset:

Data Analysis:

Decision Tree Classifier, Random Forest Classifier, Imbalanced Data

-All analysis and visualization done in Python using pandas numpy sklearn seaborn matplotlib