Data Driven Insights: Machine Learning For Terrorist Attacks Region Predication

Abstract

The use of purposeful violence for the aim of achieving political or religious goals is what is meant by the term "terrorism."
This paper's purpose is to provide a prediction about the location and nation in which terrorist strikes will occur. In
addition to this, it evaluates the effectiveness of machine learning algorithms in predicting the nation and the location
where terrorist attacks would occur. When it comes to predicting both the nation and the area, Logistic Regression has
an accuracy rate of 82%. For the purpose of this study, the Global Terrorism Database (GTD) is used. This database is
open source and contains information on terrorist incidents that have occurred all over the globe since 1970. This body
of work has the potential to be used in the future by policymakers in order to create policies and defense systems that are
capable of monitoring and forecasting terrorist operations.