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Visualizing the Global Terrorism Database

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The slides depict results of the global terrorism trends based on matplotlib and seaborn libraries.

Published in: Data & Analytics
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Visualizing the Global Terrorism Database

  1. 1. Visualizing the Global Terrorism Dataset Saurav Jha Machine Learning Engineering, FactSet mail@sauravjha.com.np
  2. 2. Data set • Available at - https://www.start.umd.edu/gtd/ • 135 columns, 181691 entries (1970-2017)
  3. 3. Visualizing geographical data - plotly
  4. 4. Asia & Europe by Attack and Target typeAsiaEurope
  5. 5. Top ranked countries & cities – bar plot Nations Cities
  6. 6. Top ranked regions & weapons – bar plot Regions Weapons
  7. 7. Top ranked groups & targets – bar plot TargetsGroups
  8. 8. Visualizing dependencies - Correlation heatmap Observations: • Suicide – Ransom amount paid • Ransom amount paid – Weapon types • Ransom amount – Individual • Weapon type – Claim mode • Gun certain – Ransom amount, etc.
  9. 9. Evaluating the correlations - jointplot • Weapon type 5 = Firearms with most kills • Weapon type 7 = Fake weapons with least kills
  10. 10. Frequency visualization – Histogram plot • Region 6 (South Asia) and 10 (Middle East & North Africa) = most sensitive. • Region 4 (East Asia) = least sensitive. • Attack type 3 (Bombing / Explosion) = most prevalent. • Attack type (Hijacking) = least prevalent.
  11. 11. Region vs Weapon Type - boxplot Observations: • Weapon type 2 = Chemical, Prevalent in region 6, i.e., South Asia • Weapon type 9 = Melee, Prevalent in region 8, i.e., Western Europe
  12. 12. • Jupyter notebook - https://github.com/Saurav0074/ds- 101/blob/master/globalTerror.ipynb • Further Read – Chasing the Trajectory of Terrorism THANK YOU!

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