Using Machine Learning Methods to Predict Patients' Survival

This project was a group project that required our team to use a variety of machine learning methods to predict the survival of patients with heart failure based on their clinical, body, and lifestyle information. Specifically, we used both unsupervised learaning (i.e., PCA and clustering) and supervised learning methods (i.e., kNN, logistic regressions, LASSO, and decision trees) on our data set. Based on the results, we also explored the best statistical models for our data set and identify the most important predictors in these models.