Premonition of Terrorist Exertion Applying Supervised Machine Learning Proficiency
H S Supriya · International Journal for Research in Applied Science and Engineering Technology · 2020
This undertaking examines an incorporating AI approach for arrangement and investigation of Global Terrorist Activity. Machine learning-based data processing is usually applied to predict acts of terrorist events by which the experts expect to urge a transparent picture of what the terrorists are pondering to accentuate defense against these organized acts. This project focuses on the prediction of terrorist activities from the Global Terrorism Database (GTD) with Supervised Machine Learning algorithms. Random Forest, k-Nearest Neighbor, Logistic Regression, Support Vector Classification, Decision trees, Linear Regression, Gaussian Naive Bayes, Linear Discriminant Analysis are adopted during this project. Finally an in-depth comparison of classification performance is presented, where classification precision ranges between 84-93% which validates the feasibility of applying machine learning to the terrorism field.