This project uses a convolutional neural network to classify clothing items in the Fashion-MNIST dataset. The data is normalized and split into training, validation, and test sets. A custom CNN architecture is trained with the Adam optimizer and sparse categorical cross-entropy loss, achieving about 90.66% accuracy on the test set. The project strengthens skills in deep learning, experiment tracking, and model evaluation.