{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/f1c8a11cd25a431da79b71eed1b65b2c\" frameborder=\"0\" width=\"1514\" height=\"1135\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1135,"width":1514,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1135,"thumbnail_width":1514,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/f1c8a11cd25a431da79b71eed1b65b2c-c8a875a9e6b2f94b.gif","duration":270.747,"title":"Building a Python Classifier for Analyzing Runtime Complexity in Big O Notation 🚀","description":"In this video, I demonstrate my web app, Biggie-D-O, which classifies Python code based on its runtime complexity in big-O notation. I showcase examples like merge sort and bubble sort, highlighting the app's speed and accuracy in providing the correct complexity analysis. The model I developed achieved an 88% F1 macro score using a deep learning approach with 1.3 billion parameters. I utilized existing datasets and conducted thorough model selection and fine-tuning. I encourage viewers to check out the links in my repository for more details and insights on the project."}