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Using tree-based machine learning models to predict diverse compost maturity via one-hot encoding: Model deployment, experimental validation, and practical application
This repository contains code and data for predicting Germination Index (GI) and Greenhouse Gas (GHG) emissions using machine learning techniques. The models are trained on a dataset containing various features related to waste management and composting processes.
The goal is to develop accurate predictive models that can help in optimizing composting processes and reducing environmental impact.
- Data preprocessing scripts
- Feature engineering techniques
- Machine learning model implementations
- Evaluation metrics and visualization tools
- Instructions for reproducing results