This project implements a fully connected neural network using only NumPy, without relying on deep learning frameworks like TensorFlow or PyTorch. The model is trained on a classification dataset using forward propagation, backpropagation, and gradient descent. The training process includes performance visualization with real-time accuracy tracking and class prediction distribution plots.