See attached fileINFS 494 PROJECT 2021 The cover story on the May 25, 2020 copy of Barron’s was...

Question

See attached fileINFS 494 PROJECT 2021 The cover story on the May 25, 2020 copy of Barron’s was about small businesses and it stated:  “Small-business owners are struggling to hang on.  Why our economic fate depends on their success”.   For your project, you will work with a dataset with information from the U.S. Small Business Administration.  You have been hired by a bank to build a data mining model that will aid them in deciding whether or not to grant a loan to a particular small business.  They are interested both in knowing whether or not to approve the loan, and in being able to explain why or why not a loan application was rejected.   The dataset for this project is AZIDORDataForProject.xlsx, and the target field is MIS_Status.  This field has a value indicating whether the loan, in the end, was paid back in full, or defaulted on some amount (charged off). [This project is to be done by you alone.] Name: __________________________________ In Sakai, you must attach: (4 points total) · this document with the answers filled out,  · (2) your final stream, and  · (2) the final Excel file(s) as you used them. Data and Type Node:  (5 points total) While the dataset has companies that have applied for loans from many states, you should build your models only for companies that are in Arizona (AZ).   Decide which columns should be used.  Make any necessary changes to the Type Node.   You will need to exclude fields that would not be appropriate for a general model that can be used in the future. · (1)  List any field you are excluding as an Input or Target and explain why. · (1)  List each field you are using as an Input and explain why it is a good field to use in your model. · (1) Paste your final Type Node here.  Be sure I can see all the fields. · (1) The number of final Input Variables was:____________ · (1) The number of data rows used in training the model was:________ For this project you will develop two models and compare them.  You want to use models that will do a good job in predicting the values of the target field.   Model 1 should be selected from:  C5.0, CHAID, C&RT, or QUEST. Model 2 should be selected from:  Neural Network, SVM-RBF, SVM-Sigmoid, SVM-Polynomial, or Logistic Regression. The intent of this model is to predict whether a loan will be Paid In Full or Charged Off (ie, it defaulted) and this model will be used by the bank in following years to decide whether or not to approve future loans.   Models:   (8 points total) · (1) List any changes made to the default settings for Model 1:    · (1) The reason for using this model was: · (1) List any changes made to the default settings for Model 2:    · (1)  The reason for using this model was: · (1) Browse the generated Model 1 and paste a picture here that best shows the browsed model. · (1) Paste the predictor importance screen here for Model 1. · (1) Browse the generated Model 2 and paste a picture here that best shows the browsed model. · (1) Paste the predictor importance screen here for Model 2. Model Results: (20 points Total) · (1) What is the best node to use for evaluating your models’ accuracy and why? · (1) What percent of correct and incorrect answers did you have in the training set for each value of the target variable in Model 1?   · (1) Paste output here that supports that answer. · (1) What percent of correct and incorrect answers did you have in the training set for each value of the target variable in Model 2?   · (1) Paste output here that supports that answer. Construct a graph that explains the impact of some input variable in Model 1. · (1) Paste that graph here. · (1) How does the graph illustrate the impact of the variable you selected? Construct a graph that explains the impact of some input variable in Model 2. · (1) Paste that graph here. · (1) How does the graph illustrate the impact of the variable you selected? · (1) What percent of correct and incorrect answers did you have in the validation set for each value of the target variable in Model 1? · (1) Paste output here that supports that answer. · (1) What percent of correct and incorrect answers did you have in the validation set for each value of the target variable in Model 2? · (1) Paste output here that supports that answer. · (1) Using the same graph that you used on the training set for Model 1, compare what it tells us using the validation set data.   · (1) Paste that graph of validation set data here. · (1) Using the same graph that you used on the training set for Model 2, compare what it tells us using the validation set data.   · (1) Paste that graph of validation set data here. · (2) Compare the model accuracy of Model 1 and Model 2 for the training and validation sets on each value of the target variable. · (1) In your opinion, which model did a better job on this data and why? Predictions: (11 points total) For each of the rows below, give the actual and predicted values for the target variable, and explain how Model 1 made its prediction. · (2) ID: 1179863003 · (2) ID: 1354865004 · (2) ID: 1381985000 · (2) ID: 2049904005 Suppose that, for each prediction you made correctly, the company that has hired you will pay $1.00.  If you predict incorrectly, the company will deduct $2.00 if your prediction was PIF and $1.00 if your prediction was ChgOff.   · (1) How much would you have made on the validation set of Model 1? · (1) How much would you have made on the validation set of Model 2? · (1) Paste a picture here of your final stream(s) here. General Application: (2 points total) · (1) Could we use this same generated model on the Oregon data? (Explain your answer.) · (1) What might be potential benefits/disadvantages of having a model predict loan defaults?

Mohd · Accepted Answer

Sheet1
	LoanNr_ChkDgt	NewExist	LnTerm	lnNoEmp	franchiseCode	UrbanRural	RevLineCr	LowDoc	LnDisburse	Ln_GrAppv	LnSBAapprv	Mis_status	$XF-Mis_status	$XFC-Mis_status
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INFS 494 PROJECT 2021 The cover story on the May 25, 2020 copy of Barron’s was about small businesses and it stated: “Small-business owners are struggling to hang on. Why our economic fate depends on...

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