Abstract:
Assume you have a credit card in your possession. Your previous spending patterns will be discovered. For example, how much money you spend, where you spend it, how often you spend it, and what you buy. If your current credit card transaction deviates from your previous spending habits, it will be suspected of fraud; otherwise, it will be treated as a legitimate transaction and fraud transactions will be alerted in the dashboard. Millions of transactions will be used to make such predictions. Distributed frameworks that can scale as the number of transactions increases are therefore employed. Spark Kafka and Cassandra are used to create this system for real-time credit card fraud detection. Preprocessing is done using Spark Machine Learning Pipeline Stages such String Indexer, Vector Slicer, Standard Scaler, and Vector Assembler. Vector Slicer, Standard Scaler and Vector Assembler is used for Preprocessing. Utilizing the Random Forest Algorithm, a Machine Learning model is produced. K-means Algorithm is used for data balancing. Automation of both Spark Machine Learning and Spark Streaming with Kafka and Cassandra is done using Apache Airflow.
