This project is a Credit Fraud Detection System designed to identify and prevent fraudulent activities in credit card transactions. Leveraging machine learning algorithms, the system analyzes transaction data to distinguish between legitimate and potentially fraudulent transactions.
In this kernel, we are going to predict whether a credit card is fraud or not using Machine Learning.
The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
Due to confidentiality issues, the input variables are transformed into numerical using PCA transformations