processing training data using Neural network (Deep learning) in Python3

I need help with processing training data using NNet (Deep learning) in Python3 from CSV here: [login to view URL] its a small part of my project that i need help with Process the Training Database using a NNet 1) Read from CSV the inputs and labels (X,Y) in the training data 2) Display at least one X sample. It is advisable to convert the X values into floats Make sure that the labels (Y) are one-hot-encoded 3) Isolate a validation data set from the training set. 4) Neural network should be a 3 layered NNet with only 1 hidden network 5) Activation should be sigmoid 6) Use batch gradient descent to solve for weights and biases At the end of each epoc, use the validation data to report on the accuracy of training 7) When learning has concluded, display error over all epocs, display validation accuracy for all epocs, display accuracy for testing data set. software: jupiter notebook based on python 3