This project focuses on the analysis and prediction of CO₂ emissions using historical data. The objective is to understand emission trends, identify key contributing factors, and apply data science and machine learning techniques to model and predict future CO₂ emission levels.
The project follows an Artificial Intelligence and Data Science workflow, combining data preprocessing, exploratory analysis, and predictive modeling to support climate and environmental decision-making.
- Imported historical CO₂ emissions datasets
- Cleaned missing, inconsistent, and noisy data
- Transformed raw data into structured formats suitable for analysis
- Normalized and scaled features where required
- Analyzed CO₂ emission trends over time
- Identified long-term patterns and fluctuations in emissions
- Used visualizations to explore relationships between variables
- Highlighted key factors influencing emission changes
- Selected relevant features affecting CO₂ emissions
- Created derived features to improve model performance
- Removed redundant and low-impact variables
- Prepared datasets for supervised learning
- Developed machine learning models to predict future CO₂ emission values
- Trained models using historical data
- Evaluated performance using appropriate regression metrics
- Compared multiple models to identify the best-performing approach
- Assessed model accuracy and generalization ability
- Interpreted predictions to extract meaningful insights
- Identified trends relevant to climate analysis and planning
- Demonstrated the value of AI-driven environmental modelling
- Enhances understanding of long-term CO₂ emission trends
- Supports data-driven environmental policy and research
- Provides a foundation for future emissions forecasting projects
- Demonstrates practical application of AI in climate studies
- Python
- Pandas
- NumPy
- Matplotlib / Seaborn
- Scikit-learn
- Jupyter Notebook