Neural Network using Pytorch
Implement a Neural Network using Pytorch
Credit Card Approval. On one of the first lectures, a credit card approval record with multiple predictors was used as an example to show the usefulness of learning from data. Here you will have the chance to create your own Neural Network based on CC data.csv. The data contains 15 predictors, and one response (approved or denied). You’ll randomly split your data into training and testing with a 80/20 ratio.
(a) Implement a Neural Network using Pytorch with the following specifications:
Input layer: Number of neurons as number of inputs (15).
Second layer: fully connected layer with N neurons.
Set the number of neurons for the second layer N , and the number of epochs, to obtain an accuracy of at least 80% in the training data.
(b) Evaluate the model with the testing data, report the accuracy; compute and print the predicted decision for every input of the testing data (approved or denied) alongside with the real labels.
