Projects
Explore my data science and machine learning projects
Real-time Revenue Analytics Platform
Developed a scalable data pipeline using Apache Kafka and AWS Lambda to ingest and process real-time sales data. Implemented a data warehousing solution with Google BigQuery to enable seamless integration with business intelligence tools. Enhanced reporting accuracy by 30% through automated ETL processes, ensuring timely insights for revenue operations.
Cloud-based Data Infrastructure Optimization
Led a project to migrate legacy data systems to a cloud-based architecture utilizing Google Cloud Platform. Designed and implemented data storage solutions with Google BigQuery, optimizing query performance by 40%. Collaborated with cross-functional teams to align data architecture with business goals, improving data accessibility and reliability.
Predictive Maintenance System for Drone Fleet
Engineered a predictive maintenance system for drone operations using Python and TensorFlow. Created data pipelines to process telemetry data, enabling early detection of potential failures. Improved operational efficiency by 25% through timely maintenance alerts and reduced downtime.
Customer Segmentation and Personalization Engine
Built a customer segmentation model using unsupervised learning techniques such as K-Means clustering. Integrated the model into the marketing platform to deliver personalized content and offers. Resulted in a 15% increase in customer engagement and a 10% boost in sales through targeted campaigns.
Anomaly Detection in Telecommunication Networks
Developed an anomaly detection framework for mobile networks using Apache PIG and LSTM models. Processed and analyzed data from over 10,000 mobile cells to identify patterns and predict anomalies. Enhanced network reliability by proactively addressing overpopulation and bandwidth issues, as published in IEEE PACRIM 2019.
My Approach
My project methodology combines rigorous data science principles with practical software engineering practices to create solutions that are both technically sound and business-focused.
Data-Driven Development
- Thorough data analysis and exploration
- Data quality and integrity validation
- Iterative model development and evaluation
- Performance metrics and continuous improvement
Engineering Excellence
- Scalable architecture design
- Cloud-native deployment strategies
- Automated testing and CI/CD pipelines
- Monitoring and observability