The goal of the project is to create a model that accurately predicts future geopolitical events by evaluating previous conflicts as reported in the global news. Data is primarily gathered through the GDelt project which accumulates world events and includes event-specific information, such as source and target demographics, Goldstein score, CAMEO codes and more. The project will consist of implementing machine learning techniques for conflict analysis using Apache Flink, PySpark, Hive and Pig. We will closely follow the framework set up by Chris Perry (Machine Learning and Conflict Prediction: A Use Case) and hope to improve upon his results by including additional features. Along with the Naive Bayes and Random Forest algorithms used by Perry, a Hidden Markov Model will also be evaluated.

The Entelodont (hell pig or terminator pig) are a species of pig-like omnivores that roamed the forests and plained of North America, Europe, and Asia. Traces have been found from the middle Eocene to early Miocene epochs.