The purpose of this work is to predict the success of a terrorist attack using approaches in machine learning. The research was carried out on the Global Terrorism Database (GTD), an open database which contains a list of terrorist activities. Eight machine learning algorithms have been applied on some selected set of features from the data set to achieve an maximum accuracy of 93%.
The algorithms implemented are :
Out of all the above mentioned algorithms Random forest proves to be the most effecient.