Building an Aviation Analytics Tool with Python, XGBoost, and Geospatial Data (feat. Mandar Chaudhari)

Can machine learning help identify regional airports that have the potential to support commercial airline service? What does it take to build an application that combines geographic information, demographic statistics, economic indicators, and aviation data into a meaningful analytical tool? And how much of that work actually involves machine learning versus traditional data engineering and software development?

In this episode, host Krish Palaniappan speaks with Mandar Chaudhari, a full-stack geospatial engineer at Land IQ and aviation researcher at George Mason University. Mandar walks through RASAT (Regional Air Service Analysis Tool), a research application developed through his work with Dr. Lance Sherry at the Center for Air Transportation Systems Research. Their conversation explores the problem RASAT addresses, its underlying architecture, the selection of machine learning algorithms, data processing pipelines, geospatial visualization, and the broader challenges of building products that solve meaningful problems.