Ÿ Pre-processed the data by dealing with the unbalanced big data (320K rows) and modifying the data types, and did EDA Ÿ Built models by using Regression (Logistic, Lasso, Causal Lasso) and Classification (KNN, Random Forest, Neural Network) methods, tuned parameters and then predicted the risk/likelihood that a random person having heart disease Ÿ Compared models by their out-of-sample performance, accuracy rate, false positive/negative rate, R^2