A project where we started our learning of Machine Learning and implementing it to a biological problem known as Coronary Artery Disease (CAD). We first applied ML algorithms to a cardiovascular risk factor dataset and predicted the risk factors, after that we implemented Navier-Stokes Equations to a neural network algorithm making it Physics-informed neural network (PINNS). We applied PINNS in our data to calculate the Wall Shear Stress (WSS) of the blood in the diseased artery (stenosis).