About

Built a batch analytics pipeline to monitor daily retail revenue and automatically detect abnormal spikes and drops. The system cleans transaction data, aggregates it into a daily time series, and applies an Isolation Forest model to identify unusual revenue patterns without labeled data.

Anomalies are classified using a rolling 7-day business context and deployed as a runnable workflow in Zerve, producing clear anomaly tables and visual outputs for quick business interpretation.