An interactive business-grade dashboard for analyzing and forecasting CO₂ emissions across African countries using Streamlit, pandas, and machine learning.
# 🌍 CO₂ Emissions Dashboard for Africa
This project is a dynamic and interactive **Streamlit-powered dashboard** that visualizes and forecasts **CO₂ emissions across African countries**. It provides actionable insights for **policy makers, entrepreneurs, researchers**, and **climate investors**.
Built with 💡 business users in mind, the dashboard highlights:
- Sector-wise emission breakdowns (transportation, energy, manufacturing, etc.)
- Time-series trends and forecasts (using linear regression)
- Country-specific heatmaps and GDP/population insights
- Clean, attractive visualizations for presentations and decision-making
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## 📊 Key Features
- 🎯 **Interactive Sector Boxes** – Click on any emission sector to drill down into trends.
- 🧠 **Machine Learning Forecasts** – Predict emissions for the next 3 years using socioeconomic data.
- 📈 **Bar Charts, Pie Charts & Line Graphs** – Beautifully styled visualizations powered by Matplotlib and Seaborn.
- 🔥 **Correlation Heatmaps** – Understand relationships between CO₂ emissions and GDP/population.
- 🪴 **Actionable Recommendations** – Green energy, climate tech, and policy innovation opportunities.
- ☁️ **Live Dataset from Hugging Face** – Auto-loads updated CSV data from the cloud.
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## 🚀 Live Demo
> Coming soon via Streamlit Cloud
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## 📦 Tech Stack
- Streamlit – For rapid web app development
- Pandas – Data loading and manipulation
- Seaborn & Matplotlib – Visualizations
- Scikit-learn – Forecasting via Linear Regression
- Hugging Face Datasets – Cloud-hosted dataset
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## 📁 Dataset Source
The data is hosted on Hugging Face:
- `co2_Emission_Africa.csv`
Fields include:
- Country, Year, Population, GDP (USD and PPP)
- Emissions by sector (Transportation, Manufacturing, LUCF, etc.)
- Total emissions (with and without LUCF)
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## 📌 How to Run
1. Clone the repo:
```bash
git clone
github.com
cd co2-emissions-dashboard