Machine Learning

Gang Reduction and Youth Development

Research for the GRYD program in Los Angeles

Financial Well Being

One of the most commonly used tools in public policy and social sciences are surveys. In the absence of directly observable actions, self-reported surveys become indispensable in identifying and classifying high-risk individuals. One such survey, which identifies individuals under financial duress, is the subject of our project. Using Support Vector Machines (SVM), a supervised machine learning method, we investigate whether indirect information from our survey dataset determines a respondent’s financial well-being.