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

Logistic Regression:

	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

Shallow NN:

	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

Random Forest:

	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

Gradient Boosting:

	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

SVC:

	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