Using data science techniques to filter and visualize the data, in this project we analyze this world bank's dataset from kaggle to draw conclusions and produce visualizations about the green house contributions of the Arab world.
Note: The Arab World consists of 22 countries in the Middle East and North Africa: Algeria, Bahrain, the Comoros Islands, Djibouti, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Mauritania, Oman, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates, and Yemen.
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Clone the repository
git clone https://github.com/Reepulse/Arab-world-data.git
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Change directory to Arab-world-data
cd Arab-world-data -
Install the required packages
pip install folium numpy pandas matplotlib
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Start the
jupyter serverjupyter notebook
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This will open
jupyter clientin your browser window
- Download the dataset by clicking on this link
- After downloading extract all the files in the
Arab-world-datafolder - Make sure
Indicators.csvis in same folder as other notebook files - Now open the
analysis.ipynband play with the notebook - I have also created a geographic map representing emissions using
folliom.create_map.ipynbis the notebook for creating map and you can view map by openingplot_data.htmlin the browser
Note: You must have jupyter notebook configured with latest version of python in your local machine for this to work. If you don't have it configured then you use anaconda to install these
Note: In this project I analyzed Indicators.csv from the word banks data set. It is only a part of the dataset, original dataset contains a lot more data. You can download the dataset for more analysis
Lots of data to analyze
Turns out that over years emissions per capita have just increased.
Turns out that Arab world countries produce 3-4 metric tons co2 per capita on average