CO₂ Emissions Predictor: Insights from World Bank Data

Overview

The CO₂ Emissions Predictor is a data-driven application designed to analyze and predict carbon dioxide (CO₂) emissions for countries worldwide using World Bank data. This project leverages key environmental, economic, and social indicators to generate insights and deliver predictions through an interactive dashboard.

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Features

• Data Insights:
  Analyze CO₂ emissions trends based on GDP, renewable energy usage, urban population, and other key indicators.

• Machine Learning Predictions:
  Predict CO₂ emissions using a trained Random Forest Regressor model with engineered features.

• Interactive Dashboard:
  An intuitive Streamlit-based dashboard for exploring global trends and generating predictions.

• Feature Engineering:
  Metrics such as Emissions Intensity (emissions per GDP) and Renewable Energy Urban Impact (renewable energy × urban population %) for enhanced analysis.

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Project Structure

CO₂ Emissions Predictor/
├── world_bank_data         # Contains cleaned datasets
├── app.py                  # Streamlit app files for the interactive dashboard
├── notebooks.ipynb         # Jupyter notebooks 
├── README.md               # Project documentation
├── requirements.txt        # Python dependencies

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## **Setup and Installation**

### **1. Prerequisites**
Ensure you have the following installed:
- Python 3.8 or higher
- pip (Python package installer)

### **2. Clone the Repository**
```bash
git clone https://github.com/osisamkay/co2-emissions-predictor.git
cd co2-emissions-predictor

3. Install Dependencies

Install required Python packages:

pip install -r requirements.txt

4. Launch the Dashboard

Run the Streamlit app:

streamlit run dashboard/app.py

Open the provided URL in your browser to access the dashboard.

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How It Works

Data Pipeline

1. Data Loading:
   Load World Bank data for economic and environmental indicators.

2. Feature Engineering:

   • Emissions Intensity: CO₂ emissions per GDP.
   • Renewable Energy Urban Impact: Contribution of renewable energy and urbanization.

3. Machine Learning:
   The Random Forest Regressor is trained to predict CO₂ emissions using the engineered features.

4. Interactive Visualization:
   A dashboard enables users to explore country-specific trends and predictions.

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Key Insights

• Countries with higher GDP typically exhibit varying CO₂ emissions, depending on energy sources and urbanization.
• Renewable energy usage reduces emissions intensity, especially in urbanized nations.
• Strong relationships exist between economic indicators and environmental outcomes.

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Technologies Used

• Data Processing: pandas, NumPy
• Visualization: matplotlib, seaborn, Streamlit
• Machine Learning: scikit-learn (Random Forest Regressor)
• Deployment: Streamlit

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Future Improvements

• Dynamic Data Updates: Real-time World Bank data integration.
• Advanced Models: Incorporate Gradient Boosting or neural networks for enhanced accuracy.
• User Customization: Allow users to add custom datasets or indicators.
• Geospatial Analysis: Visualize data geographically to identify regional trends.

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Contributing

We welcome contributions! Please:

1. Fork this repository.
2. Create a feature branch (git checkout -b feature-name).
3. Commit changes (git commit -m "Add feature").
4. Push to your fork (git push origin feature-name).
5. Submit a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Contact

For questions or suggestions, please reach out:

• Email: osisami.oj@gmail.com
• GitHub: osisamkay

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