Image Classification for Skin Cancer test outputs

To solve this image classification problem I have followed these steps:

1. Loading and pre-processing of data.
2. Defining model architecture.
3. Training and fitting the model.
4. Evaluating the model.

I have used for this project Google Colaboratory and stored all the dataset and files in Google Drive.

I have used a simple architecture with 2 convolutional layers, one dense hidden layer and an output layer.

I have got accuracy of 81% for 10 epoch. will have to check different combinations of epoch and architecture. Also I have kept the image size small for faster processing.