This project focuses on data visualization and analysis using global commodity trade statistics. The notebook includes data cleaning, analysis, and visualization steps to provide insights into the dataset.
In notebook, as we have analysed the dataset throughly we used all the data available.
But as the dataset was too big, we had issues using it on Tableau. So we created a subset of the data for our Tableau visualization.
Dataset Link: Data Set
You can use the data in Main_Data.zip file to run the notebook and the data in Small_Data.zip file to run the Tableau visualization.
To run the code in our notebook and reproduce the visualizations, you need the following Python libraries installed: Also to run our Tableau visualization, you need to have Tableau Desktop installed on your machine.
matplotlibnumpypandasseaborn
You can install them using pip:
pip install matplotlib numpy pandas seaborn- Data Cleaning
- Initial preprocessing to clean and prepare the dataset for analysis.
- Data Analysis
- Detailed exploration of the dataset with descriptive statistics and visualizations.
- Data Visualization
- Visualizations to highlight key insights from the dataset.
The notebook generates several visualizations that highlight key insights from the global commodity trade statistics dataset.
- If any errors occur, ensure all library dependencies are installed and up-to-date.
- We will be sending an already activated notebook, so you can also redownload it!
- Anıl Dervişoğlu, 150220344
- Ömer Faruk Zeybek, 150220743