Road Accident Severity Prediction Using Context-Aware Feature Representation and Deep Learning Techniques

A road accident severity prediction system that uses context-aware feature representation and deep learning techniques.

A road accident severity prediction system that uses context-aware feature representation and deep learning techniques. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

The main parts are dataset preparation and augmentation, model design and training, evaluation with standard metrics, plus error analysis, and a simple interface or API to try the model. The reference build uses Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.