SUPPORT VECTOR MACHINE VS. LOGISTIC REGRESSION FOR PREDICTING MORTGAGE DEFAULTS
Mortgage loan providers estimate the credit risks it caries when approving a mortgage loan to their clients. Further, defaulting a mortgage loan is a risk that has been calculated through decades using statistical models. By using entries at the time of a mortgage application, the goal of the thesis is to com- pare the accuracy between logistic regression and Support Vector Machine in predicting
