The project aims to classify images of German traffic signs into their respective categories using machine learning techniques. The dataset used is the German Traffic Sign Benchmark, which consists of over 50,000 images and more than 40 classes.

It involves multiple steps, including importing necessary packages, downloading the dataset, data preprocessing, analyzing the dataset, and ultimately constructing a neural network to recognize and classify the traffic signs.

The data preprocessing step includes resizing images to a standard dimension to ensure consistency when feeding them into the model. The ultimate goal is to train a model that can accurately identify the type of traffic sign from a given image.

Key stack used: