Today, the problem of protecting against fraudulent actions by intruders is one of the most urgent tasks for most banks. One of the possible ways to solve this problem is to use machine learning models.
In this project, we will use an Artificial Neural Network model to detect fraudulent credit card transactions.
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.
Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Source: https://www.kaggle.com/mlg-ulb/creditcardfraud
Identify fraudulent credit card transactions.