This project explores global CO2 emissions data to identify trends, rank top-emitting countries, and visualize key insights using a combination of Python (Jupyter Notebook), Power BI, and Tableau.
- Analyze CO2 emissions trends over time.
- Rank countries by emissions for specific years.
- Create interactive dashboards for exploration.
- Use geospatial maps to highlight regional differences.
- Python (Jupyter Notebook):
- Libraries:
pandas,matplotlib,geopandas,squarify. - Used for data preprocessing and exporting cleaned
.csvfiles.
- Libraries:
- Power BI:
- Dashboards for time-series trends and country comparisons.
- Tableau:
- Advanced interactive visualizations, including geospatial maps.
- Downloaded and cleaned CO2 emissions data using Python.
- Exported cleaned data for use in Power BI and Tableau.
- Power BI:
- Time-series trends of CO2 emissions.
- Bar charts for top emitters by year.
- Interactive dashboards for exploring data.
- Tableau:
- Geospatial maps for regional emissions analysis.
- Interactive storytelling charts.
- Python: Automated data fetching, cleaning, and transformation.
- Power BI: Interactive dashboards for business insights.
- Tableau: Geospatial and dynamic visualizations for deeper insights.
- projectwork.ipynb:
- Contains the Python code for data cleaning and preprocessing.
- co2.pdf:
- A detailed report summarizing key insights.
- Tableau.png:
- Screenshot of Tableau visualizations for CO2 emissions.
- Combine data from multiple sources for a more comprehensive analysis.
- Use machine learning to predict future CO2 emissions trends.
- Open
projectwork.ipynbin Jupyter Notebook for data preprocessing. - Load cleaned
.csvfiles into Power BI or Tableau for visualization. - Review the report (
co2.pdf) for key takeaways.