A production-ready, real-time machine learning system for cryptocurrency price prediction. Based on lambda architecture, it ingests market data, performs feature engineering for technical indicators, trains predictive models following MLOps best practices

Real-time Stream and Batch Processing of Simulated Twitter Data using Apache Kafka and Apache Spark. This project implements a Lambda architecture to efficiently handle large-scale data streams, simulating Twitter API performance. It demonstrates the power of Kafka and Spark in both real-time and batch processing scenarios.