README: Trophism Classification by Machine Learning and Deep Learning

This project involves clustering analysis, AutoML for model selection, training engineering, model assessment, and computational time analysis for a classification task.

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Dependencies

To ensure smooth execution of the project, install the following dependencies:

Python Version

• Python >= 3.12.6

Libraries

• Data Manipulation and Analysis:
  • pandas >= 1.3
  • numpy >= 1.21
• Visualization:
  • matplotlib >= 3.4
  • seaborn >= 0.11
• Machine Learning:
  • scikit-learn >= 0.24
  • h2o >= 3.38
• Deep Learning:
  • torch >= 1.10
  • tensorflow >= 2.6
• Clustering Analysis:
  • scikit-learn (included above)
• Bioinformatics:
  • biopython >= 1.79
• Natural Language Processing:
  • transformers >= 4.12
• Utilities:
  • tqdm >= 4.62

To install these dependencies, you can use the following command

pip install -r requirements.txt

Installation Instructions

1. Set Up Virtual Environment

It is recommended to use a virtual environment to manage dependencies:

python -m venv myenv
source myenv/bin/activate  # For Linux/Mac
myenv\Scripts\activate     # For Windows

Repository Structure

• data/
  • ai_ready/ # Preprocessed datasets ready for analysis.
• notebooks/
  • Clustering_Analysis.ipynb # Clustering analysis and visualization.
  • AutoML_Hyperparameter_Tuning.ipynb # AutoML and hyperparameter optimization.
  • Model_Training_Assessment.ipynb # Training strategies and model performance.
  • Computational_Time_Analysis.ipynb # Computational time evaluation.
• Research_Relevance.md # Relevance and context of the research.
• requirements.txt # List of dependencies for the project.
• README.md # This file.