Analytical Approaches to Improve the Defensive Asylum Process at the United States Southern Border

This Major Qualifying Project encompasses an analysis of the United States defensive asylum process with the goal of providing information to assist decision makers for immigration policy. Currently, there are over one million pending immigration cases that include asylum seekers who are waiting for a hearing. Through the use of data and regression analysis, queuing theory, simulation, and optimization, the team developed a web-based tool to aid in resource allocation at the United States southern border. The web tool takes these relationships and user input data to output a sector-by-sector allocation of judicial resources to minimize time in system, queue size, and costs

Analytical Approaches to Macroeconomic Forecasting: A Study of Profits through Machine Learning and Deep Learning

With a basis on the analytical framework of Levy and Kalecki\u27s Corporate Profits Equation, this MQP uses Machine Learning and Deep Learning to provide a forecast for aggregate corporate profits in the United States. The tool used to deliver this forecast was the RapidMiner Software and the data source was the Federal Reserve Bank of St. Louis. The independent variable was Aggregate Profits for the following quarter and the dependent variables were Investment, Dividend, Household Saving, Net Government Saving, ROW Saving and the Statistical Discrepancy. Making use of these predictions and relying on economic theory, this paper explores the repercussions of assumptions made through the Cambridge Controversies until today, regarding the relationship between the working class and the elite