https://github.com/gaizkiaadeline/classification-of-living-organism-kingdoms

Classification of Living Organism Kingdoms Based on Codon Content in mRNA using Machine Learning. Implementing several machine learning models for classification, including Decision Tree, Random Forest, and SVM.
https://github.com/gaizkiaadeline/classification-of-living-organism-kingdoms

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Classification of Living Organism Kingdoms Based on Codon Content in mRNA using Machine Learning. Implementing several machine learning models for classification, including Decision Tree, Random Forest, and SVM.

• Host: GitHub
• URL: https://github.com/gaizkiaadeline/classification-of-living-organism-kingdoms
• Owner: gaizkiaadeline
• Created: 2024-10-16T05:15:54.000Z (23 days ago)
• Default Branch: main
• Last Pushed: 2024-10-16T05:20:12.000Z (23 days ago)
• Last Synced: 2024-10-17T19:06:22.751Z (21 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 46.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Classification of Living Organism Kingdoms using Machine Learning

This project applies machine learning techniques to classify living organisms into their respective kingdoms based on specific features. The notebook includes:

- Data Preprocessing: Cleaning and transforming the dataset for model training.

- Machine Learning Models: Implementing several machine learning models for classification, including Decision Tree, Random Forest, and Support Vector Machine (SVM).

- Model Evaluation: Evaluating model performance using metrics such as accuracy, precision, recall, and confusion matrix.

Key Features:

- Uses a dataset of living organisms classified into different kingdoms (e.g., Animalia, Plantae, Fungi).

- Preprocessing includes data normalization and handling missing values.

- Compares the performance of various machine learning algorithms.

- Visualizes the model results to better understand the classification outcomes.