https://github.com/siam29/hybrid-feature-engineering-and-ensemble-learning

In this ML project, I proposed a methodology that provided an outperformed performance compared to another existing paper. For the comparison here focused mainly on F1, accuracy, AUC, and ROC score. This methodology provides a 99.96% accuracy score and 90.05% F1 score. 
https://github.com/siam29/hybrid-feature-engineering-and-ensemble-learning

feature-selection keras-tensorflow machine-learning matplotlib python scikit-learn

Last synced: 8 days ago
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In this ML project, I proposed a methodology that provided an outperformed performance compared to another existing paper. For the comparison here focused mainly on F1, accuracy, AUC, and ROC score. This methodology provides a 99.96% accuracy score and 90.05% F1 score. 

• Host: GitHub
• URL: https://github.com/siam29/hybrid-feature-engineering-and-ensemble-learning
• Owner: siam29
• Created: 2024-07-27T10:16:17.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-27T10:18:54.000Z (7 months ago)
• Last Synced: 2024-12-06T04:16:53.135Z (2 months ago)
• Topics: feature-selection, keras-tensorflow, machine-learning, matplotlib, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.49 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Hybrid-Feature-Engineering-and-Ensemble-Learning

In this ML project, I proposed a methodology that provided an outperformed performance compared to another existing paper. For the comparison here focused mainly on F1, accuracy, AUC, and ROC score. This methodology provides a 99.96% accuracy score and 90.05% F1 score.