Credit card fraud detection is becoming really problematic now a days with increasing online purchases there are increasing Cyber attacks. Identifying them is very important. In this document we will go through how we did the analysis of a data set containing fraud and non fraud transactions and using different ML algorithms how are we able to predict the fraud cases.
Credit Card Fraud Detection with Machine Learning is a process of data investigation by a Data Science team and the development of a model that will provide the best results in revealing and preventing fraudulent transactions. This is achieved through bringing together all meaningful features of card users’ transactions, such as Date, User Zone, Product Category, Amount, Provider, Client’s Behavioral Patterns, etc.
In this Model we will be able to detect most of the Fraud cases using Random forest algorithm. We generally use Random Forest Algorithm(RFA) for finding the fraudulent transactions and the accuracy of those transactions. This algorithm is based on supervised learning algorithm where it uses decision trees for classification of the dataset. After classification of dataset a confusion matrix is obtained. The performance of Random Forest Algorithm is evaluated based on the confusion matrix. The results obtained from processing the dataset gives accuracy of about 92%