Hand Written Digit Prediction Using Neural Network

Handwritten grants a skill away from micro to predict the calligraphy digits picked escape starting points such as profile, test, meet screens, and others. It is an arduous duty as a gadget model as typed in numerals is not at all ideal to must live made with a lot of distinct flavours. In this paper, we will use various ML algorithms like CNN, SVM, Decision Tree, Gaussian Naive Bayes, etc.We import information from the image net dataset for both testing and training the machine design. MNIST includes 70000,28*28 images of handwritten digits ranges 0 to 9.