/GTD-Analysis

A data science centered, semester project on Global Terrorism Database(GTD).

Primary LanguageJupyter NotebookMIT LicenseMIT

GTD-Analysis

A data science centered, semester project on Global Terrorism Database(GTD).

Overview

Project Phases:

  1. Ideation
  2. Exploratory Data Analysis
  3. ML Model Design or Selection
  4. Model Deployment

Datasets

Primary Dataset:

Global Terroism Dataset™ (GTD) is an open-source database including information on terrorist events around the world from 1970 through 2020 (with annual updates planned for the future). Unlike many other event databases, the GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 200,000 cases.

Data collection methodology(cookbook): https://www.start.umd.edu/gtd/downloads/Codebook.pdf

Download Dataset: https://www.start.umd.edu/gtd/contact/download

Fill in the form and get access on email.

Dataset Features

  1. Unique Event ID
  2. Date
  3. Country | State/Province | City
  4. Target | Target Type
  5. Type of Attack(s)
  6. Longitude and Latitude Location
  7. Vitim(s) Name
  8. Weapon Used

Many other features, total around ~135, it would be too wordy to list them all here. Read the cookbook, too long for me lol, exploring the dataset files would make you understand it better.

Secondary Dataset:

Another dataset, OnWar.com was used for analysis of wars and their time periods aound the world. We further incorporated our findings into our "" case study.

Exploratory Data Analysis

Case Studies(Subject to change and add justification):

  1. Total Attacks over the year (Use some unique plots)
  2. Total Attacks by country (Heat map)
  3. Total Attacks by country (Top 10)
  4. Cities with Most deaths (Top 10)
  5. Cities with Most Attacks (Top 10)
  6. Types of Terror Attacks (Numbers) Exlposives, Firearms, Flamables, Biological, Chemical, Knives/Swords(Find a category), )
  7. Types of Terror Attacks (Continent/country)
  8. Targets Hit (Military + Police vs Civilians)
  9. Targets Hit (Healtcare, Religious, Security Forces, Education and Residential)
  10. Attacks by group
  11. Number of Attacks by Religious vs Unaffiliated vs Random
  12. Terrorist groups by country
  13. Top 5 terrorist group and their attacks (Map) [Card of info]
  14. War overlap with terrorist attacks (Matplotlib animation)
  15. Killings over 1000, attacks + Reasoning, Actions taken
  16. Suicide Attacks

Model

(To be decided)

File Details

AnalysisNotebook.ipynb > Exploratory Data Analysis portion

AllTerroristGroups.csv > All group names in the dataset and number of attacks

Technologies Used

Python 3.11

Jupyter Notebook

Gradio

Azure