https://github.com/ricardorobledo/spamemailclassification
Spam email classification using machine learning (Random Forest, SVC, Logistic Regression, etc.) with data balancing techniques (SMOTE, BorderlineSMOTE, ADASYN). Final calibrated Random Forest model achieves ROC-AUC 0.982 and PR-AUC 0.979 on the Spam Email Classification dataset.
https://github.com/ricardorobledo/spamemailclassification
imbalanced-data imbalanced-learning numpy pandas python3 sklearn
Last synced: about 1 month ago
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Spam email classification using machine learning (Random Forest, SVC, Logistic Regression, etc.) with data balancing techniques (SMOTE, BorderlineSMOTE, ADASYN). Final calibrated Random Forest model achieves ROC-AUC 0.982 and PR-AUC 0.979 on the Spam Email Classification dataset.
- Host: GitHub
- URL: https://github.com/ricardorobledo/spamemailclassification
- Owner: RicardoRobledo
- Created: 2025-08-29T12:33:07.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-08-29T12:40:18.000Z (5 months ago)
- Last Synced: 2025-08-29T15:54:08.661Z (5 months ago)
- Topics: imbalanced-data, imbalanced-learning, numpy, pandas, python3, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 160 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0