Predictive Analysis on Global Terrorism

Machine learning is being extensively used in various domains to solve problems which require the classification of data and predictive modeling. The current scenario depicts that the world is experiencing a threat of terrorism globally which is evident from the increased terrorist attacks in the past decades. This paper proposes a method to implement machine learning algorithms to predict and classify certain patterns or elements related to any attack. Although the occurrence of any attack is not predictable as it involves a major role of human psychology and mindset, there are certain elements which can be predicted to a certain extent. In this paper we proposed to predict the type of weapons which may have been used and the terrorist organization which may have been behind the attack using machine learning algorithms. The different classifiers were compared and evaluated for better results.

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Narula, Meenakshi and Kumar, Siddharth and Kaul, Saksham and KS, Saurav, Predictive Analysis on Global Terrorism (April 6, 2020). Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020, Available at SSRN: https://ssrn.com/abstract=3569598 or http://dx.doi.org/10.2139/ssrn.3569598