CreditCardFraudDetectionML

Simulating Credit Card Fraud Detection in Real Time using Machine Learning with Highly Imbalanced Data

In this Repository we will deal with the following:

1. How is Over-sampling useful in dealing with highly imbalanced datasets?
2. How Random Forest, among the range of evaluated machine learning algorithms, could predict frauds with a F1-Score of 81 percent?
3. Integrating Big Data tools like KAFKA, SPARK and DBFS with Random Forest to simulate a real time credit card fraud detection system.
4. Testing this system over high volume and velocity and measure performance.