Machine Learning and Statistical Analysis Techniques on Terrorism
Author ORCID Identifier
Frontiers in Artificial Intelligence and Applications
Terrorism is a major issue facing the world today. It has negative impact on the economy of the nation suffering terrorist attacks from which it takes years to recover. Many developing countries are facing threats from terrorist groups and organizations. This paper examines various terrorist factors using data mining from the historical data to predict the terrorist groups most likely to attack a nation. In this paper we focus on sampled data primarily from India for the past two decades and also consider International database. To create meaningful insights, data mining, machine learning techniques and algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine, Ensemble methods, Random Forest Classification are implemented to analyze comparative based classification results. Patterns and predictions are represented in the form of visualizations with the help of Python and Jupyter Notebook. This analysis will help to take appropriate preventive measures to stop Terrorism attacks and to increase investments, to grow the economy and tourism.
Classification, Data Mining, Global Terrorism Database (GTD), Machine Learning
Rajesh, P.; Babitha, D.; Alam, Mansoor; Tahernezhadi, Mansour; and Monika, A., "Machine Learning and Statistical Analysis Techniques on Terrorism" (2020). NIU Bibliography. 616.
Department of Electrical Engineering