Gitbook version: https://pr0x2b.github.io/masters_thesis_on_global_terrorism/

Global Terrorism Database Interactive Dashboard

A visualization of k-means clustering on terrorist attack locations

The project analyses and visualises global terrorist attacks over the time frame of 1970 to 2019. Points out and analyses trends of over 2,00,000 terror attacks, their attackers and studies various other features

Used the Global Terrorism Database to Explore Features of Suicide Bombings

KMeans Clustering on Global Terrorism Database

Exploratory Data Analysis on dataset Global Terrorism.As a defence or security analyst we have to find hot zone of terrorism.

Collaborative visualization task

In an era marked by global security challenges, the "TAFRAS" emerges as a cutting-edge solution to tackle the ever-evolving threat of terrorism. The project is grounded in the urgent need for predictive systems that can anticipate, assess, and mitigate potential terrorist activities.

Visualization/ kernels in R using datasets on Kaggle.

This project is an attempt to use START Global Terrorism Dataset to target a regression problem and identify region for probable terror attack. Predict number of casualties in a terrorist attack, based on a custom metric.

This project used machine learning to understand characteristics of terrorist groups that engage in suicide bombings.

This is an information visualization project of Global Terrorism Database with Yingrong Mao.

A comprehensive analysis of the GTD, to uncover global terrorism patterns, trends, and impacts through data-driven analysis. Involves rigorous analysis of most used attack & weapon types; favourite targets; yearly distribution of casualties, no. of attacks, success rates, and more - both holistic and for specific countries and terror organizations

This repository presents analyses conducted in R on The Troubles using the Global Terrorism Database

The Spark Foundation Task-------->The project analyses and visualises global terrorist attacks over the time frame of 1970 to 2019. Points out and analyses trends of over 2,00,000 terror attacks, their attackers and studies various other features

This repository contains an Exploratory Data Analysis (EDA) on the Global Terrorism Dataset. The EDA was performed using Python's Pandas, NumPy, Matplotlib, and Seaborn libraries to identify the hot zones of terrorism and gain valuable insights from the dataset.

A web-based visualization of the Global Terrorism Database using D3.js.