https://github.com/siam29/ensemble-majority-voting-hard

In this project, we implemented an ensemble learning approach using majority voting (hard voting) with five machine learning classifiers: DT, RF, XGBC, ANN, and KNN. The ensemble model achieved an impressive accuracy score of 99.95% and an F1 score of 85.51%.
https://github.com/siam29/ensemble-majority-voting-hard

credit-card-fraud ensemble-learning machine-learning matplotlib pandas scikit-learn

Last synced: about 1 month ago
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In this project, we implemented an ensemble learning approach using majority voting (hard voting) with five machine learning classifiers: DT, RF, XGBC, ANN, and KNN. The ensemble model achieved an impressive accuracy score of 99.95% and an F1 score of 85.51%.

• Host: GitHub
• URL: https://github.com/siam29/ensemble-majority-voting-hard
• Owner: siam29
• Created: 2024-07-27T10:28:43.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-07-27T10:37:43.000Z (9 months ago)
• Last Synced: 2025-02-01T16:10:24.530Z (3 months ago)
• Topics: credit-card-fraud, ensemble-learning, machine-learning, matplotlib, pandas, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 75.2 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Ensemble-majority-voting-Hard

In this project, we implemented an ensemble learning approach using majority voting (hard voting) with five machine learning classifiers: DT, RF, XGBC, ANN, and KNN. The ensemble model achieved an impressive accuracy score of 99.95% and an F1 score of 85.51%.