Using Real-Life imbalanced fraud detection dataset to experiment several machine learning classification models, while dealing with imbalanced sets using multiple sampling techniques. Metrics of Evaluation: Confusion Matrix, Precision, Recall, F1-Score, ROC, AUC and Classification Cost
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Using Real-Life imbalanced fraud detection dataset to experiment several machine learning classification models, while dealing with imbalanced sets using multiple sampling techniques.
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