This Project uses Transfer learning to classify Heart Attack Risk. Ensemble methods were used to "learn" more information about the dataset, and a Feedforward Neural Network was implemented from scratch to obtain the final results. The FNN Implemented various activation functions, weight initialization methods, to allow for maximum customizability.
Python Version: 3.11.3 Packages Used: numpy, pandas, matplotlib, seaborn, scikit-learn, itertools, scikit-plot, ipykernel, jupyter
Random states were used to allow for reproducible results