This thesis evaluates the effects of including automated event data for interstate conflict prediction. Automated event data are web-scraped news stories converted into data and they may allow conflict models to increase their performance. Accurate models can then be used for early-warning purposes. To predict three separate problems, tree ensemble classifiers were used. The three outcomes to be predicted were the occurrence of interstate conflict, its onset, and its escalation. They were predicted globally at the dyad month level, meaning monthly for every country pair, using data from 1995 to 2014. The feature set consisted of eleven structural, slow-changing variables, and 268 event features, which were event counts on a dyad month according to event type. The analysis showed that event data did not increase performance. This held across all three prediction problems. Additionally, it was found that the models for occurrence and escalation and performed well and decently well, respectively, but that the models for conflict onset performed poorly. In conclusion, event data needs further testing in different constellations to be effective in interstate conflict prediction. It seems likely, however, that effective prediction for policy guidance is possible, given the model performance of the occurrence and escalation models.