Machine Learning Models for Prediction of Terrorist Attacks: A Comparative Analysis in Terrorism Prone Regions

Terrorism presents distinct data collection challenges, making open-source datasets of terrorist incidents particularly appealing. Nevertheless, prior data sources predominantly concentrated on international events, involving individuals or groups from one nation targeting locations in another. The research involves the development of a machine-learning model aimed at forecasting regions susceptible to terrorist activities. Various machine-learning methods are analyzed for identifying unusual behavior patterns indicative of terrorism, emphasizing anomaly detection in security contexts.