Global Terrorism Analysis

Overview

This project involves data analysis on the Global Terrorism Dataset, leveraging Natural Language Processing (NLP) techniques to analyze incident descriptions and classify terrorist organizations.

Data Analysis

Description

The analysis focuses on exploring and understanding patterns within the Global Terrorism Dataset, aiming to derive insights and trends related to terrorist incidents worldwide.

Tools and Libraries

• Pandas
• NumPy
• Matplotlib
• Seaborn

Analysis Steps

1. Data Loading: Importing the Global Terrorism Dataset.
2. Exploratory Data Analysis (EDA): Exploring key statistics, trends, and visualizing the data.
3. Geospatial Analysis: Mapping incidents to understand geographical patterns.
4. Temporal Analysis: Analyzing trends over time.
5. Statistical Insights: Extracting statistical information to better understand the dataset.

Natural Language Processing (NLP)

Description

NLP techniques are applied to analyze the textual descriptions of each incident in the dataset. This involves extracting meaningful information and patterns from the unstructured text.

Tools and Libraries

• BERT
• WordCloud

NLP Steps

1. Text Preprocessing: Cleaning and preparing the incident descriptions.
2. Tokenization: Breaking down text into tokens.
3. Sentiment Analysis: Understanding the sentiment of incident descriptions.
4. WordCloud Visualization: Visualizing the most frequent words in the dataset.

Classification of Terrorist Organizations

Description

Classification algorithms are employed to categorize and label terrorist organizations based on specific attributes or features.

Tools and Libraries

• PyTorch

Classification Steps

1. Feature Engineering: Identifying relevant features for classification.
2. Model Selection: Choosing a suitable classification algorithm.
3. Training and Evaluation: Training the model and evaluating its performance.
4. Confusion Matrix: Analyzing the classification results.

Results

Summarize key findings and insights from the data analysis, NLP, and classification processes.

Usage

Provide instructions on how to replicate the analysis and use the code in this repository.

Contributing

If you'd like to contribute to this project, please follow the guidelines in [CONTRIBUTING.md].

License

This project is licensed under the [MIT License] - see the [LICENSE.md] file for details.