Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models are good for times series data that are very volatile. In this project, I developed and made predictions with different GARCH models using Apple stock prices. Time series chart Link to project 5

A Support Vector Machine (SVM) can be imagined as a surface that defines a boundary between various points of data which represent examples plotted in multidimensional space according to their feature values. The goal of an SVM is to create a flat boundary, called a hyperplane, which leads to fairly homogeneous partitions of data on either side. In this project, I showed some examples of SVM using Quadratic Programming. Plots of exponential loss when delta = 1; 0.