GitHub - MylieMudaliyar/Credit-Card-Fraud-Detection: Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.

Performance metrics (classification report) for each model on the balanced dataset:

	Precision	Recall	F1-Score	Support
Not Fraud	0.93	0.99	0.96	69
Fraud	0.99	0.93	0.96	73
Accuracy			0.96	142
Macro Avg	0.96	0.96	0.96	142
Weighted Avg	0.96	0.96	0.96	142

	Precision	Recall	F1-Score	Support
Not Fraud	0.86	1.00	0.93	69
Fraud	1.00	0.85	0.92	73
Accuracy			0.92	142
Macro Avg	0.93	0.92	0.92	142
Weighted Avg	0.93	0.92	0.92	142

	Precision	Recall	F1-Score	Support
Not Fraud	0.91	1.00	0.95	69
Fraud	1.00	0.90	0.95	73
Accuracy			0.95	142
Macro Avg	0.95	0.95	0.95	142
Weighted Avg	0.96	0.95	0.95	142

	Precision	Recall	F1-Score	Support
Not Fraud	0.93	0.94	0.94	69
Fraud	0.94	0.93	0.94	73
Accuracy			0.94	142
Macro Avg	0.94	0.94	0.94	142
Weighted Avg	0.94	0.94	0.94	142

	Precision	Recall	F1-Score	Support
Not Fraud	0.93	0.97	0.95	69
Fraud	0.97	0.93	0.95	73
Accuracy			0.95	142
Macro Avg	0.95	0.95	0.95	142
Weighted Avg	0.95	0.95	0.95	142

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