This document presents a skin disease prediction system built using a deep learning model. The system was trained on the Harvard HAM dataset containing images of 7 common skin diseases. Data augmentation techniques like rotation, shearing, zooming were used to improve the quality and size of the dataset. A convolutional neural network model with convolution, pooling, ReLU and fully connected layers was developed using Keras. The model achieved an accuracy of 82% and was integrated into a web-based user interface to allow users to upload images for disease prediction. Further improvements to increase accuracy require enhancing the model with more data and computational resources.