Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/datasqlsantosh/terrorist-attacks-data-analysis

This data analysis portfolio aims to analyze and visualize global terrorist attacks data to gain insights into trends, patterns, and factors associated with terrorist incidents worldwide.
https://github.com/datasqlsantosh/terrorist-attacks-data-analysis

colab-notebook excel google google-sheets matplotlib python seaborn

Last synced: about 1 month ago
JSON representation

This data analysis portfolio aims to analyze and visualize global terrorist attacks data to gain insights into trends, patterns, and factors associated with terrorist incidents worldwide.

Awesome Lists containing this project

README 

        
# Terrorist Attacks Data Analysis Portfolio



## Overview

This data analysis portfolio aims to analyze and visualize global terrorist attacks data to gain insights into trends, patterns, and factors associated with terrorist incidents worldwide. The analysis utilizes Python programming language, Google Colab environment for code execution, and an Excel sheet file containing the terrorist attacks dataset.



## Data Source

The dataset used in this analysis is sourced from [insert data source here, (https://www.kaggle.com/datasets/willianoliveiragibin/terrorism-in-world). The dataset includes information on various attributes of terrorist attacks such as date, location, attack type, target type, casualties, etc.



## Methodology

1. **Data Loading**: The Excel sheet file containing the terrorist attacks dataset is loaded into a Pandas DataFrame using Python.

2. **Data Cleaning and Preprocessing**: Data cleaning techniques are applied to handle missing values, inconsistencies, and anomalies in the dataset. Data preprocessing steps such as encoding categorical variables and feature engineering may also be performed.

3. **Exploratory Data Analysis (EDA)**: Exploratory data analysis techniques are employed to gain insights into the characteristics and distributions of the data. This includes summary statistics, visualizations (e.g., histograms, bar plots, heatmaps), and hypothesis testing.

4. **Visualization**: Various data visualization techniques are utilized to illustrate trends, patterns, and relationships in the data. This includes time series analysis, geographical mapping, and thematic visualizations.

5. **Statistical Analysis**: Statistical methods may be applied to test hypotheses, identify correlations, and measure associations between variables.



## Tools and Libraries Used

- Python 

- Google Colab (Jupyter Notebook environment)

- Pandas

- Matplotlib

- Seaborn



## Usage

1. **Setup Environment**: Open the provided Google Colab notebook in Google Colab or any Python environment that supports Jupyter notebooks.

2. **Upload Dataset**: Upload the Excel sheet file containing the terrorist attacks dataset to your Google Colab session or provide the file path.

3. **Execute Code Cells**: Execute each code cell in the notebook sequentially to load the data, perform analysis, and generate visualizations.

4. **Interpret Results**: Review the generated visualizations and analysis results to gain insights into global terrorist attacks patterns and trends.



## Conclusion

The analysis of the terrorist attacks dataset provides valuable insights into the nature, frequency, and impact of terrorist incidents worldwide. By leveraging Python programming and data analysis libraries, this portfolio demonstrates the capability to extract actionable insights from large-scale datasets.



---



Feel free to customize and expand upon this template based on the specific details of your analysis and portfolio project. Include any additional sections or information that you deem relevant to effectively communicate the objectives and outcomes of your data analysis.