Neeraj Pandey

The carbon market is set to reach 1T dollars by 2030, yet 84% of offsets fail to deliver real climate benefits. Verification still relies on sparse site visits and self-reported data. This poster shows a Python workflow that audits carbon projects using satellite imagery and ML, detecting...

Let’s make your functions smarter with decorators, simplify operations using lambdas, save memory with generators, and handle resources like a pro with context managers. We’ll mix clear explanations with hands-on practice - so you can write code that’s cleaner, faster, and feels Pythonic.

Python has grown into a key tool for creating art and fashion, moving beyond data work to help make clothing, jewelry, and interactive designs. This talk looks at how Python brings together artists, designers, and coders to build projects that mix tradition with tech. Through live coding, we'll...

Have you ever wondered how to turn your Python games into lightning-fast web experiences without compromising speed? In this session, overcome the browser barrier and learn how to create powerful game engines that smoothly connect desktop and web platforms using Pygame, pygbag, and WebAssembly.

Learn about the convergence of PyGame in Python and WebAssembly. In this talk, explore how we can turn Python based games into web sensations. Understand the essential of game development with PyGame, from animation to interaction and how PygBag bridges these creations to the web with...

You might have encountered beautiful animations and data storytelling through many educators and experimentalists online and wondered if it’s only done in javascript or could also be done by Python.

Explore the importance of visibility and observability in Python programming in this session. Learn how these crucial tools aid in debugging and troubleshooting, boosting application reliability and performance. Discover various techniques and tools for monitoring Python applications, including...

Learn how model drifts in the production environment affects our machine learning models and how to track and assess them in Python so that the model remains relevant in production and makes fair and unbiased predictions over time.