Predicting Germination Index & Greenhouse Gases using Machine Learning

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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.

The repository includes:

• Data preprocessing scripts
• Feature engineering techniques
• Machine learning model implementations
• Evaluation metrics and visualization tools
• Instructions for reproducing results

Notice: This repository is for research purposes only, and some of the research data has not posted yet. Please cite the original article if you use this code or data in your work.