The challenge is to recognize fraudulent credit card transactions so that the customers of credit card companies are not charged for items that they did not purchase.
To get a better feeling of which algorithm would perform best on our data, let’s quickly spot-check some of the most popular classification algorithms:
1.Logistic Regression,
-
Linear Discriminant Analysis,
-
K Nearest Neighbors (KNN),
-
Classification Trees,
-
Support Vector Classifier,
-
Random Forest Classifier
-
XGBoost Classifier
This Credit card fraud detection projcet is performed using :
✅Decision tree
📌Isolation Forest Algorithm.
Fraud detection is a complex issue that requires a substantial amount of planning before throwing machine learning algorithms at it. Nonetheless, it is also an application of data science and machine learning for the good, which makes sure that the customer’s money is safe and not easily tampered with.
Future work will include a comprehensive tuning of the Random Forest algorithm I talked about earlier. Having a data set with non-anonymized features would make this particularly interesting as outputting the feature importance would enable one to see what specific factors are most important for detecting fraudulent transactions.