I built this Excel dashboard to analyze lung disease risk factors and treatment outcomes using healthcare data. The project focuses on identifying patterns between smoking habits, disease risk, and treatment success. Using Pivot Tables, charts, and interactive filters, the dashboard helps visualize key insights and supports better understanding of health trends.

Tools Used: Microsoft Excel, Pivot Tables, Data Cleaning, Data Visualization.

Problem: Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals.

Solution: This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.

Very real problem. Manual invoice processing is still surprisingly common and becomes a huge bottleneck as companies scale. Automating extraction and validation with AI can save a lot of operational time and reduce errors significantly.