# CO2 Emissions Prediction & Anomaly Detection

**Technologies:** Python, PyTorch, AWS (Athena, SageMaker, Grafana), TensorFlow, XGBoost  

## Objective
Develop a predictive model for CO2 emissions and improve anomaly detection using LSTM-based models.

## Key Contributions
- Optimized an LSTM-based autoencoder model for enhanced anomaly detection.
- Built a CO2 emissions prediction model with ensemble methods (XGBoost, Random Forest).
- Deployed models using AWS, integrating Athena and Grafana for real-time data visualization.

## Outcome
Enabled real-time environmental monitoring, showcasing skills in model optimization and cloud analytics.