Creditcard Fraud Detection System.

The detailed analysis of credit card fraudulent data detection system.

Dataset: Anonymized credit card transactions labeled as fraudulent or genuine Download

Algorithm Used:

1. Simple Logistic regression
2. Logistic Regression with Undersampling
3. Synthetic Minority Over Sampling Technique (SMOTE) with gini as the critertionn of best split.
4. Synthetic Minority Over Sampling Technique (SMOTE) with entropy as the critertionn of best split.

Brief Summary of performance.

No.	Model	Precision	Recall	Accuracy	Conclusion
1	Logistic Regression	0.73270	0.62398	0.99896	Rejected due to low recall.
2	Undersampling & Logistic Regression	0.072	0.87	0.98	Rejected due to very low precision.
3	SMOTE with Gini criteria for best split	0.95528	0.95528	0.99985	Best model so far. But next model provides better results. Hence, rejected.
4	SMOTE with entropy criteria for best split	0.96177	0.97154	0.99988	Accepted.