identifying and classifying handwritten digits ranging from 0 to 9. Trained on the MNIST dataset, which consists of 60,000 training images and 10,000 test images, the model achieves a commendable accuracy of 95.7222% on the test set. The neural network architecture, comprising two hidden layers with ReLU activation and a softmax activation output layer, showcases its ability to generalize well to new, unseen data. The evaluation process involves assessing the model's predictions against the ground truth labels, demonstrating its effectiveness in recognizing diverse handwritten digit patterns..
Overall, the model's successful classification of handwritten digits underscores its practical utility in various applications, such as digit recognition in documents and automated postal code processing.
