Expert answer:Neural Nets

Solved by verified expert: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.This assignment uses a grading rubric. Please review the rubric prior
to beginning the assignment to become familiar with the expectations
for successful completion.
screen_shot_2018_04_08_at_7.02.38_pm.png

screen_shot_2018_04_08_at_7.02.51_pm.png

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Female
Male
Male
Male
Male
Male
Male
Female
Male
Male
Female
Female
Female
Female
Male
Female
Female
Female
Female
Female
Male
Male
Male
Male
Female
Female
Female
Female
Female
Male
University
University
University
University
University
University
University
Graduate School
University
Graduate School
Graduate School
University
University
University
Graduate School
Graduate School
Graduate School
Graduate School
University
University
University
University
University
University
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
University
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
High Scool
High Scool
High Scool
High Scool
University
University
University
University
University
High Scool
Single
Single
Single
Single
Single
Single
Single
Single
Married
Single
Single
Married
Single
Married
Single
Single
Single
Single
Married
Married
Married
Married
Married
Married
Married
Married
Married
Single
Single
Single
Single
Single
Single
Single
Single
Single
Single
Married
Married
Married
Married
Single
Single
Single
Single
Single
Single
31
31
31
37
38
29
29
29
37
28
28
57
25
47
28
28
28
28
36
40
32
32
32
51
29
43
37
28
28
28
28
25
29
29
29
29
29
41
41
41
41
22
22
22
22
22
33
3 Mths Late
3 Mths Late
3 Mths Late
Nothing Due
On Time
Nothing Due
Nothing Due
Nothing Due
1 Mth Late
On Time
Nothing Due
On Time
On Time
Nothing Due
On Time
On Time
On Time
On Time
1 Mth Late
On Time
2 Mths Late
2 Mths Late
2 Mths Late
1 Mth Late
Nothing Due
On Time
Nothing Due
On Time
On Time
On Time
On Time
On Time
On Time
On Time
On Time
On Time
On Time
2 Mths Late
2 Mths Late
2 Mths Late
2 Mths Late
Nothing Due
Nothing Due
Nothing Due
Nothing Due
Nothing Due
On Time
200
200
201
202
203
204
204
204
204
205
206
207
208
209
210
211
212
213
213
213
213
214
215
215
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
236
10000
10000
180000
350000
150000
70000
70000
70000
70000
150000
100000
100000
100000
300000
80000
520000
270000
400000
400000
400000
400000
240000
300000
300000
300000
320000
250000
50000
20000
240000
360000
240000
50000
60000
50000
20000
180000
220000
500000
280000
80000
50000
150000
20000
100000
50000
50000
Male
Male
Male
Male
Female
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Female
Female
Female
Male
Female
Female
Male
Female
Male
Female
Female
Female
Male
Male
Female
Female
Female
Male
Male
Female
Male
Male
Male
Female
Female
High Scool
High Scool
University
Graduate School
University
University
University
University
University
Graduate School
Graduate School
High Scool
University
High Scool
Graduate School
Graduate School
University
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
University
University
University
Graduate School
University
Graduate School
University
High Scool
University
University
University
Graduate School
High Scool
Graduate School
University
Graduate School
Graduate School
University
Graduate School
Graduate School
Single
Single
Married
Married
Married
Married
Married
Married
Married
Other
Single
Married
Single
Single
Single
Single
Married
Married
Married
Married
Married
Married
Single
Single
Single
Single
Single
Married
Single
Single
Single
Married
Single
Single
Married
Single
Single
Single
Single
Married
Single
Married
Married
Single
Married
Single
Single
33
33
47
41
31
37
37
37
37
30
47
50
27
34
26
35
42
39
39
39
39
38
28
28
28
27
36
31
25
24
27
32
25
23
48
46
35
47
30
46
28
47
40
27
29
26
26
On Time
On Time
Nothing Due
Nothing Due
On Time
Nothing Due
Nothing Due
Nothing Due
Nothing Due
On Time
Nothing Due
Nothing Due
Nothing Due
On Time
Nothing Due
Nothing Due
Nothing Due
Nothing Due
Nothing Due
Nothing Due
Nothing Due
On Time
On Time
On Time
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Nothing Due
Nothing Due
Nothing Due
Nothing Due
Nothing Due
On Time
On Time
Nothing Due
2 Mths Late
Nothing Due
Nothing Due
1 Mth Late
Nothing Due
On Time
On Time
1 Mth Late
Nothing Due
On Time
1 Mth Late
2 Mths Late
Nothing Due
Nothing Due
236
236
236
237
238
239
240
241
242
242
242
243
243
243
243
244
245
246
247
248
249
250
251
252
252
252
252
253
254
255
256
256
256
256
257
257
257
257
258
259
260
260
260
260
260
261
262
50000
50000
50000
100000
360000
110000
130000
80000
50000
50000
50000
150000
150000
150000
150000
180000
160000
150000
160000
20000
20000
290000
50000
110000
110000
110000
110000
220000
50000
80000
30000
30000
30000
30000
80000
80000
80000
80000
200000
110000
100000
100000
100000
100000
100000
210000
230000
Female
Female
Female
Female
Male
Male
Male
Female
Female
Female
Female
Male
Male
Male
Male
Male
Female
Male
Female
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Female
Female
Female
Female
Female
Male
Male
Graduate School
Graduate School
Graduate School
University
Graduate School
Graduate School
Graduate School
High Scool
University
University
University
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
Graduate School
University
University
High Scool
University
Graduate School
Graduate School
Graduate School
Graduate Sch …
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