Peer-graded Assignment Part 5  Modeling Credit Card Default

Peer-graded Assignment Part 5  Modeling Credit Card Default Risk and Customer Profitability

 

Project Title *
Give your project a descriptive title

Modeling Credit Card Default Risk and Customer Profitability

 

What is your predictive model?

a. Describe the arithmetic clearly so that another learner could implement your model on new standardized input data if they wished.

b. Give an example of the score you would assign the following applicant, whether they would be approved or rejected for a credit card and why.

a) The main thing we should do is examine the connection of the factors, and recognize which are the most important in the model. At that point, we should distinguish the boundaries or coefficients that will go with the factors of said model, utilizing the direct relapse procedure in Excel found in the course. The most applicable parametric qualities are: Years at a current business: – 0.19 pay over the previous year: – 0.08 Current Visa obligation: – 0.19 Current car obligation: – 0.07 Then, with these coefficients and considering the relationship, we will make our model. Which is: SCORE = 0.19 * Years at a current boss – 0.08 * salary over the previous year – 0.19 * Current Mastercard obligation 0.07 * Current vehicle obligation b)Considering that by upgrading AUC, we got the limit for the base expense/occasion as 0.25. A score beneath – 0.04 for instance will be resolved as a contrary test, which interprets as a monetarily productive individual, who could be affirmed for a Visa.

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Give an example of the score you would assign the following applicant, whether they would be approved or rejected for a credit card and why.

b)Considering that by streamlining AUC, we got the edge for the base expense/occasion as 0.25. A score underneath – 0.04 for instance will be resolved as a contrary test, which interprets as a monetarily beneficial individual, who could be endorsed for a Mastercard.

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What would the bank’s average profit per applicant be (net profits divided by 200) when using your predictive model on the Training Set?

The average profit per applicant will be 794$ on the training set.

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What is the incremental financial value per applicant of your model over no model on the Training Set?

The incremental financial value per applicant of your model over no model on the Training Set is $654.41

Evaluate your model on the Test Set data. How confident are you that your model does not over-fit the Training Set data? The only basis to evaluate over-fitting is to give the same metrics on the Test Set and Training Set, and compare them.

The model has an extraordinary performance in both information tests, since the relationship is very much applied, and the parametric coefficients discovered are right, this infers that the AUC is high and doesn’t change impressively, notwithstanding keeping up the assessed costs per occasion.

Evaluate your model on the Test Set data. How confident are you that your model does not over-fit the Training Set data?

A. Choose between three broad degrees of confidence: “very” “somewhat” or “not at all.” (Note that “not at all” is still an acceptable answer if you give persuasive reasons for why you chose this answer).

B. Explain the evidence your degree of confidence is based upon. Your explanation should include the test set profits and training set profits per applicant.

How much confidence to have in the model must relate to the relationship between the profits-per-applicant on the Training Set and the Test Set

a) Very b) Because the AUC in both information tests is high and steady, it is away from of the proficiency of the model. Also, it keeps up a decent assessed benefit edge on the grounds that the expenses per occasion are not essentially changed.