FayCodes/kenya-co2-emissions-forecast

# Kenya CO₂ Emissions Forecasting for Climate Action ## Project Overview This project aligns with **SDG 13: Climate Action**, using machine learning to forecast Kenya’s carbon emissions. By analyzing historical data, it provides insights that help policymakers, industries, and environmental organizations make data-driven decisions to reduce CO₂ output and implement sustainable policies. --- ## Machine Learning Approach This project uses **Linear Regression**, a model suited for capturing long-term trends in emissions data. By training on historical CO₂ levels, the model forecasts emissions for: - 2025 - 2030 - 2040 These predictions provide a simple roadmap for understanding future emission trends. --- ## Results Summary | Year | Predicted CO₂ Emissions (Kilotons) | |------|-------------------------------------| | 2025 | 21,090.73 | | 2030 | 23,661.43 | | 2040 | 28,802.84 | These projections highlight the urgency for sustainable interventions. --- ## Ethical Considerations - **Data Accuracy:** Ensuring reliable emissions data for meaningful forecasting - **Policy Impact:** Predictions should support sustainable decisions, not economic exploitation - **Bias & Fairness:** Linear models may oversimplify real-world environmental complexity --- ## Project Structure - `data/` → Contains cleaned emissions dataset (`kenya_emissions.csv`) - `src/` → Scripts for data processing, training, and prediction (`train.py`, `predict.py`) - `models/` → Trained regression model (`linear_regression.pkl`) - `pitch_deck/` → Presentation materials - `report/` → Full project documentation --- ## Report Google Docs Report: docs.google.com --- ## How to Run the Project ### 1. Install dependencies ```bash pip install -r requirements.txt