I have two small programming assignments (PA1/PA2). I have done PA1 and I am not sure about it if is it correct or not. PA2 is an extend of PA1. so, please find PA1 and PA2 descriptions and what I submitted for PA1.I need the code for PA2

The purpose of this assignment is to perform neural nets classification, interpret the results, and analyze whether or not the information generated can be used to address a specific business problem.For this assignment, you will use the "Credit Card Defaults Oversampled" data set from the Topic Materials. Note that this data set includes the same information as the Topic 5 assignment data set, however, the default cases have been randomly oversampled to balance the defaulters vs. the nondefaulters. This is one method to increase the accuracy of a rare outcome (i.e., default).You are an analyst for a credit card company. Management wants to know if there are any early signs that indicate whether customers will default on their credit cards. If these indicators can be identified, then more scrutiny can be placed on customer transactions in an effort to avoid losses. For this project, management is interested in very accurate predictions. Management would like to receive a list of ranked variables related to default, however, understanding the specific relationships between the predictors and the likelihood of default is secondary.Question 1: Partition the data to create a training data set (70%) and test data set (30%).Question 2: Build a neural network using the Multilayer Perceptron method with the training data and "Default" as the target. Make sure to output predictor importance. Include the "Default Neural Network" output when submitting the answer.What is the ranking of the variables? Include the "Predictor Importance" output when submitting the answer.What is the accuracy of the model when using the training and test data? Include the "Misclassification Table" outputs when submitting the answer.Question 3: Rerun the model but only use the top six variables.What is the accuracy of the model when using the training and test data? Include the "Misclassification Table" outputs when submitting the answer. Did the accuracy improve? Explain why.Produce a ROC curve when using the training and test data and interpret the results of the ROC curves. What do the curve and the 45-degree line represent?Question 4: Explain how the selected model can be used to solve the business problem. What are the advantages and disadvantages when building a neural net model vs. the simpler classification model like the one completed in the Topic 5 assignment?General Requirements:Submit the answers to Questions 1-4 as a Word document.APA format is not required, but solid academic writing is expected.