A Capstone Project at UC Berkeley: Project Report
Our project aims to enhance urban planning decision-making by introducing a low-code framework that simplifies the creation of data-driven tools. Specifically, we target two critical challenges as proof of concept: developing an application for predicting traffic flow by streaming data transformations in cloud environments, and utilizing a low-code approach to build digital twins. We constructed a machine-learning model and created an intuitive dashboard for data analysis. This endeavor seeks to alleviate traffic bottlenecks and demonstrate the potential of enabling data scientists to develop full-stack applications without data engineering expertise, thereby driving innovation in urban planning.
