This paper focuses on developing a traffic detection system using Altair’s RapidMiner Studio (a no-code platform built for data science and machine learning) and eventually deploying it on a compact module capable of handling the computations of deep learning like the Nvidia Jetson Nano. The system aims to detect traffic density levels in real-time using images captured from a video stream. The RapidMiner Studio platform is used to pre-process the images, extract features, and train a machine learning model for classification. The trained model is to be deployed on the Jetson Nano, which is a low-cost, power-efficient edge computing device that can perform real-time object detection. The system is tested on a variety of vehicles in a traffic scenario and achieves high accuracy in detecting traffic density levels. The results demonstrate the feasibility of using RapidMiner Studio for developing a real-time traffic detection system.