This project focuses on building a cloud-based retail analytics solution using GCP. Historical sales data is ingested into Cloud Storage and BigQuery, transformed using Dataflow, and analyzed for seasonal trends and demand forecasting. Machine learning models are trained using Vertex AI to predict future sales, and interactive dashboards are created in Looker to support business decision-making. The project reflects real-world retail analytics systems used by large enterprises.

This project involves designing an end-to-end churn prediction pipeline for a subscription-based business. Customer interaction and transaction data is processed using Dataproc and BigQuery, followed by feature engineering and model training in Vertex AI. Automated ML pipelines and model monitoring are implemented to ensure continuous performance. The solution demonstrates how enterprises proactively retain customers using predictive analytics.

This project focuses on processing real-time IoT sensor data using Pub/Sub and Dataflow. Streaming data is stored and analyzed in BigQuery, while anomaly detection models are deployed using Vertex AI. Dashboards visualize real-time metrics and alerts. The project mirrors industrial IoT monitoring systems used in manufacturing and smart infrastructure environments.