This project is based on the Exploratory Data Analysis of Global Terrorism. I have used various kinds of basic plots like Matplotlib and Seaborn to give an initial look of the data.
- This repository presents Global Terrorism Dataset Exploratory data analysis.
- With the help of this project we can see some useful insights and make decisions.
- Introduction
- Motivation
- Project Goal
- Dataset
- Installation
- Library Used
- Author
- Visualization Screenshots
- Credits
- Lessons Learned
- About Me
- Technical Skills
- Contributers
- Show Your Support
The scope of this project is to drill down the terrorist events around the world from 1970 through 2015
The primary objectives are...
- To identify and highlight the geographical and temporal patterns of the terrorism,
- To discover the main parameters of a successful terrorist attack, and
- To allow the user to customize the analysis and to explore the data in the most interactive way.
** The idea behind the project is to find out how the terrorism has developed in the Western world and whether we need to build tall walls to protect ourself against future threats. We chose our topic to be more global oriented, because
It enables aggregation on many geographical levels including the globe, regions, countries, states, and cities It is very diversified and encapsulates many interesting attributes It has both temporal and geographical data
Our idea is to take the Global Terrorism Database and perform a comprehensive analysis on it in order to get insight into the terrorism and create tools which let the user to explore the data interactively. The dataset is semistructured so it requires a lot of cleaning and preprocessing tasks in order to make it accessible for the entire project, hence we use some of the key techniques introduced in the course. First of all, we format and clean the data. Afterwards, we compress it to the client-side friendly form to require less space and make the UI more responsive. We then create many kinds of charts , each having its own function and key objectives.
This project is based on Global Terrorism Exploratory Data Analysis to get insights about the Most attacked country, region , year and many more things** The data here i used is taken from LGMVIP Internship and the data is from since 1970 to 2015.
The dataset is very comprehensive and contains a lot of terrorism-related information. We downloaded the entire dataset Global Terrorism Database, available from LGMVIP Internship Task. It contains 1,81,691 terrorist attacks x 135 features, and takes 187.1+ MB of disk space. It's worth to mention that it is almost completely encoded (strings/long numbers to short numbers). To decode the dataset we looked at the codebook available here. After exploring the codebook we discovered some columns to be redundant, or not relevant, which we removed. See the corresponding notebook Cleaning Data for further details on how we approached.
We ended up work on 19 columns, which contain the quantitative as well as the qualitative information of the main interest. After decoding, cleaning, filtering, and encoding steps, we've got 46,556 rows x 23 columns, or equivalently 7.1+ MB of disk space.
Install pandas , numpy , matplotlib, seaborn, plotly
!pip install pandas !pip install numpy !pip install matplotlib !pip install seaborn !pip install plotly 
I learned a lot of new things, some of them are given below ---
- Firstly, I feel more comfortable about NumPy, Pandas, Matplotlib and Seaborn after completion of this project.
- I am more confident about my Exploratory Data analysis Process from this task.
Thank you so much @LetsGrowMore for this internship.
I'm a Data Science Enthusiast and Aspiring Data Analyst
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Python
MySQL, SQL Server, Mongo DB
Exploratory Data analysis
Tableau , Power BI
Pandas, Numpy, Matplotlib, Seaborn, Plotly, Scipy, Dask
Supervised / Unsupervised
Excel , Word , Powerpoint
Contributions are always welcome! Please give your important and valuable review.
Give a ⭐️ if this project helped you!
If you have any feedback, please reach out to me at -Linkdin