This project investigates an integrated machine learning approach for classification and analysis of global terrorist activity. In this project, we aim to make the following three contributions – 1) exploration of supervised machine learning approaches as a novel technique in the study of terrorist activity; 2) development of a model that classifies historical events in the Global Terrorism Database (GTD) that, at present, have yet to be attributed to a responsible party; and 3) release of a new dataset, QFactors_Terrorism, that integrates event-specific features derived from the GTD with population-level demographic data from open sources like the World Bank and United Nations. Using this new dataset, a random forest model was trained that classifies the actor responsible for an identified incident with up to 68% accuracy. This project makes no claim on the ability to forecast or predict future terrorist activity – rather, it is intended to highlight the importance of a machine learning approach that, when integrated with domain area expertise, can augment study of complex social issues.