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Utilized SparkML and Scikit-Learn train several machine learning models for distinguishing fraudulent and legitimate transactions. The machine learning models are then utilized to make predictions on Kafka-generated real-time data streams. Built an interface for displaying these predictions in real-time using the Streamlit framework.
Created a SparkML RandomForest model to predict total employee compensation. Queried data with SparkSQL, ran PySpark scripts to run EDA, pre-process data, and train model achieving with 0.98 R2 score.