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.
The primary objectives of this EDA are as follows:
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Top Ten Affected Countries: Identify and list the top ten countries most affected by terrorist attacks.
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Top Ten Affected Regions: Determine and present the top ten affected regions.
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Top Ten Affected States: Discover and display the top ten affected states within these regions.
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Top Ten Affected Cities: Highlight the top ten affected cities in the dataset.
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Top Ten Attacking Modes: Identify the top ten attacking modes used by terrorists.
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Top Ten Targets of Terrorists: Explore and list the top ten types of targets that terrorists aim for.
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Top Ten Subtypes of Targets: Analyze and present the top ten subtypes of targets terrorists focus on.
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Top Dangerous Gangs/Terrorist Groups: Find and list the top dangerous gangs or terrorist groups involved in these attacks.
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Top Chosen Weapons: Discover and document the top chosen weapons used by terrorists in their attacks.
The dataset used for this analysis was obtained from Kaggle, which provides comprehensive information on terrorist incidents worldwide.
Global_Terrorism_EDA.ipynb: This Jupyter Notebook file contains the Python code for the EDA, including data loading, cleaning, analysis, and visualization.
- Pandas
- NumPy
- Matplotlib
- Seaborn
The Jupyter Notebook includes various data visualizations to provide insights into the global terrorism dataset. Charts, graphs, and plots have been used to present the findings effectively.
The EDA revealed valuable insights about terrorism, including the top affected countries, regions, states, cities, attacking modes, targets, subtypes of targets, dangerous groups, and chosen weapons. The results can help in understanding the patterns and hot zones of terrorism worldwide.