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SAS代写-EMESTER 3
时间：2021-07-31
SEMESTER 3 2020/21
COURSEWORK BRIEF:
Module Code: MANG6458 Assessment: Individual Coursework Weighting: 100
Module Title: Credit Scoring & Data Mining
Module Leader: Christophe Mues
Submission Due Date: @ 16:00 Friday 13th August 2021 Word Count: 2000
Method of Submission: Electronic via Blackboard Turnitin ONLY
(Please ensure that your name does not appear on any part of your work)
Any submitted after 16:00 on the deadline date will be subject to the standard University late penalties (see below),
unless an extension has been granted, in writing by the Senior Tutor, in advance of the deadline.
University Working Days Late: Mark:
1 (final agreed mark) * 0.9
2 (final agreed mark) * 0.8
3 (final agreed mark) * 0.7
4 (final agreed mark) * 0.6
5 (final agreed mark) * 0.5
More than 5 0
This assessment relates to the following module learning outcomes:
A. Knowledge and Understanding A1. Understand the potential of KDD and data mining for developing
scorecards.
B. Subject Specific Intellectual and Research Skills B1. Work with software to develop credit scoring solutions; develop a
scorecard using data mining techniques.
C. Transferable and Generic Skills C1. Critically analyse practical difficulties that arise when implementing
scorecards; understand the cross-fertilisation potential to other
business contexts (e.g. fraud detection, CRM).
3
Coursework Brief:
Question 1 (65 marks)
The dataset ‘Credit data.xlsx’ contains data on 10,000 borrowers and whether they subsequently experienced serious
delinquency (see variable ‘SeriousDlqin2yrs’). Assume the lender now wishes to use this data to build a credit scoring
model that predicts serious delinquency based on the other variables. The dataset contains the following variables:
1.1 Carefully pre-process the dataset by considering the following activities:
• Exploratory data analysis.
• Missing value handling (if any), including a suitable analysis of missing values and justification of the chosen
method.
• Outlier detection and treatment (if any), with appropriate analysis/justification.
• Binning the variables (if deemed useful)
• Coding the variable bins using Weights of Evidence.
• Splitting the data set into a training and test set.
Variable Name Description
SeriousDlqin2yrs Person experienced 90 days past due delinquency or worse
RevolvingUtilizationOfUnsecuredLines
Total balance on credit cards and personal lines of credit except real estate and no installment debt
like car loans divided by the sum of credit limits
age Age of borrower in years
NumberOfTime30-59DaysPastDueNotWorse Number of times borrower has been 30-59 days past due but no worse in the last 2 years.
DebtRatio Monthly debt payments, alimony,living costs divided by monthy gross income
MonthlyIncome Monthly income
NumberOfOpenCreditLinesAndLoans Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards)
NumberOfTimes90DaysLate Number of times borrower has been 90 days or more past due.
NumberRealEstateLoansOrLines Number of mortgage and real estate loans including home equity lines of credit
NumberOfTime60-89DaysPastDueNotWorse Number of times borrower has been 60-89 days past due but no worse in the last 2 years.
NumberOfDependents Number of dependents in family excluding themselves (spouse, children etc.)
SEMESTER 3 2020/21
1.2 Build an intuitive and predictive scorecard using a logistic regression classifier and report the following:
• The most important variables
• The impact of the variables on the target
• The performance of the model. Use various performance metrics and discuss their relationship if any.
Compare this scorecard with the result of a Random Forest model run over the data. Discuss your results.
Why do banks often use Logistic Regression as their classifier? What do banks win and lose by doing this?
In terms of software, you are expected to use SAS Enterprise Miner. Carefully report the various steps of your
methodology and discuss your results in a rigorous way!
NOTE: It is unlikely that different students will come up with the exact same parameter estimates. Special
consideration will be given to submissions whose estimates are identical.
Question 2 (35 marks)
Find an academic paper published in 2020 or later (based on online or print publication date) discussing a real-life
application of data mining or credit scoring. It is important that the dataset analysed in the paper consists of real-life
(not artificial) data. The suggested publication outlets in which to look for a suitable paper are:
• Management Science
• Operations Research
• INFORMS Journal on Computing
• INFORMS Journal on Applied Analytics
• Journal of Machine Learning Research
• European Journal of Operational Research
• ICDM (The IEEE International Conference on Data Mining)
• NeurlPS (Conference on Neural Information Processing Systems)
• KDD (ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
Note: if you would decide to select a paper from elsewhere, please ensure that it is of sufficiently high quality and
makes a novel contribution to the area.
Once you have found an appropriate paper, report the following in separate subsections:
• Title, authors and complete citation (e.g. journal name, volume/issue, year, …)
• The data mining problem considered
• The data mining techniques used
• The results reported
• A critical discussion of the model and results (assumptions made, shortcomings, limitations, …).
Make sure you demonstrate that you understand what the article is all about and are able to provide a critical
discussion.
Do not copy and paste from the article. Using Turnitin, this will be easily detected!
SEMESTER 3 2020/21
Nature of Assessment: This is a SUMMATIVE ASSESSMENT. See ‘Weighting’ section above for the percentage that this
assignment counts towards your final module mark.
Word Limit: +/-10% either side of the word count (see above) is deemed to be acceptable. Any text that exceeds an
additional 10% will not attract any marks. The relevant word count includes items such as cover page, executive
summary, title page, table of contents, tables, figures, in-text citations and section headings, if used. The relevant word
count excludes your list of references and any appendices at the end of your coursework submission.
You should always include the word count (from Microsoft Word, not Turnitin), at the end of your coursework
submission, before your list of references.
Title/Cover Page: You must include a title/ cover page that includes: your Student ID, Module Code, Assignment Title,
Word Count. This assignment will be marked anonymously, please ensure that your name does not appear on any part
of your assignment.
References: You should use the Harvard style to reference your assignment. The library provide guidance on how to
reference in the Harvard style and this is available from: http://library.soton.ac.uk/sash/referencing
Submission Deadline: Please note that the submission deadline for Southampton Business School is 16.00 for ALL
assessments.
Turnitin Submission: The assignment MUST be submitted electronically via Turnitin, which is accessed via the
individual module on Blackboard. Further guidance on submitting assignments is available on the Blackboard support
pages.
It is important that you allow enough time prior to the submission deadline to ensure your submission is processed
on time as all late submissions are subject to a late penalty. We would recommend you allow 30 minutes to upload
your work and check the submission has been processed and is correct. Please make sure you submit to the correct
assignment link.
You will know that your submission has completed successfully when you see a message stating ‘Congratulations –
your submission is complete…’. It is vital that you make a note of your Submission ID (Digital Receipt Number). This
is a unique receipt number for your submission, and is proof of successful submission. You may be required to
provide this number at a later date. We recommend that you take a screenshot of this page, or note the number
down on a piece of paper. You should also receive an email receipt containing this number, and the number can be
found after submitting by following this guide. This method of checking your submission is particularly useful in the
event that you don’t receive an email receipt.
You are allowed to test submit your assignment via Turnitin before the due date. You can use Turnitin to check your
assignment for plagiarism before you submit your final version. See “Viewing Your Originality Report” for guidance.
Please see the Module Leader/lecturer on your module if you would like advice on the Turnitin Originality report.
The last submission prior to the deadline will be treated as the final submission and will be the copy that is
assessed by the marker.
It is your responsibility to ensure that the version received by the deadline is the final version, resubmissions after
the deadline will not be accepted in any circumstances.
Important: If you have any problems during the submission process you should contact ServiceLine immediately by
email at Serviceline@soton.ac.uk or by phone on +44 (0)23 8059 5656.
Late Penalties: Further information on penalties for work submitted after the deadline can be found here.
Special Considerations: If you believe that illness or other circumstances have adversely affected your academic
performance, information regarding the regulations governing Special Considerations can be accessed via the
Calendar: http://www.calendar.soton.ac.uk/sectionIV/special-considerations.html
SEMESTER 3 2020/21
Extension Requests: : Extension requests along with supporting evidence should be submitted to the Student Office
as soon as possible before the submission date. Information regarding the regulations governing extension requests
can be accessed via the Calendar: http://www.calendar.soton.ac.uk/sectionIV/special-considerations.html
Academic Integrity Policy: Please note that you can access Academic Integrity Guidance for Students via the Quality
Handbook: http://www.southampton.ac.uk/quality/assessment/academic_integrity.page?. Please note any
suspected cases of Academic Integrity will be notified to the Academic Integrity Officer for investigation.
Feedback: Southampton Business School is committed to providing feedback within 4 weeks (University working days).
Once the marks are released and you have received your feedback, you can meet with your Module Leader / Module
Lecturer / Personal Academic Tutor to discuss the feedback within 4 weeks from the release of marks date. Any
additional arrangements for feedback are listed in the Module Profile.
Student Support: Study skills and language support for Southampton Business School students is available at:
http://www.sbsaob.soton.ac.uk/study-skills-and-language-support/.
学霸联盟
COURSEWORK BRIEF:
Module Code: MANG6458 Assessment: Individual Coursework Weighting: 100
Module Title: Credit Scoring & Data Mining
Module Leader: Christophe Mues
Submission Due Date: @ 16:00 Friday 13th August 2021 Word Count: 2000
Method of Submission: Electronic via Blackboard Turnitin ONLY
(Please ensure that your name does not appear on any part of your work)
Any submitted after 16:00 on the deadline date will be subject to the standard University late penalties (see below),
unless an extension has been granted, in writing by the Senior Tutor, in advance of the deadline.
University Working Days Late: Mark:
1 (final agreed mark) * 0.9
2 (final agreed mark) * 0.8
3 (final agreed mark) * 0.7
4 (final agreed mark) * 0.6
5 (final agreed mark) * 0.5
More than 5 0
This assessment relates to the following module learning outcomes:
A. Knowledge and Understanding A1. Understand the potential of KDD and data mining for developing
scorecards.
B. Subject Specific Intellectual and Research Skills B1. Work with software to develop credit scoring solutions; develop a
scorecard using data mining techniques.
C. Transferable and Generic Skills C1. Critically analyse practical difficulties that arise when implementing
scorecards; understand the cross-fertilisation potential to other
business contexts (e.g. fraud detection, CRM).
3
Coursework Brief:
Question 1 (65 marks)
The dataset ‘Credit data.xlsx’ contains data on 10,000 borrowers and whether they subsequently experienced serious
delinquency (see variable ‘SeriousDlqin2yrs’). Assume the lender now wishes to use this data to build a credit scoring
model that predicts serious delinquency based on the other variables. The dataset contains the following variables:
1.1 Carefully pre-process the dataset by considering the following activities:
• Exploratory data analysis.
• Missing value handling (if any), including a suitable analysis of missing values and justification of the chosen
method.
• Outlier detection and treatment (if any), with appropriate analysis/justification.
• Binning the variables (if deemed useful)
• Coding the variable bins using Weights of Evidence.
• Splitting the data set into a training and test set.
Variable Name Description
SeriousDlqin2yrs Person experienced 90 days past due delinquency or worse
RevolvingUtilizationOfUnsecuredLines
Total balance on credit cards and personal lines of credit except real estate and no installment debt
like car loans divided by the sum of credit limits
age Age of borrower in years
NumberOfTime30-59DaysPastDueNotWorse Number of times borrower has been 30-59 days past due but no worse in the last 2 years.
DebtRatio Monthly debt payments, alimony,living costs divided by monthy gross income
MonthlyIncome Monthly income
NumberOfOpenCreditLinesAndLoans Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards)
NumberOfTimes90DaysLate Number of times borrower has been 90 days or more past due.
NumberRealEstateLoansOrLines Number of mortgage and real estate loans including home equity lines of credit
NumberOfTime60-89DaysPastDueNotWorse Number of times borrower has been 60-89 days past due but no worse in the last 2 years.
NumberOfDependents Number of dependents in family excluding themselves (spouse, children etc.)
SEMESTER 3 2020/21
1.2 Build an intuitive and predictive scorecard using a logistic regression classifier and report the following:
• The most important variables
• The impact of the variables on the target
• The performance of the model. Use various performance metrics and discuss their relationship if any.
Compare this scorecard with the result of a Random Forest model run over the data. Discuss your results.
Why do banks often use Logistic Regression as their classifier? What do banks win and lose by doing this?
In terms of software, you are expected to use SAS Enterprise Miner. Carefully report the various steps of your
methodology and discuss your results in a rigorous way!
NOTE: It is unlikely that different students will come up with the exact same parameter estimates. Special
consideration will be given to submissions whose estimates are identical.
Question 2 (35 marks)
Find an academic paper published in 2020 or later (based on online or print publication date) discussing a real-life
application of data mining or credit scoring. It is important that the dataset analysed in the paper consists of real-life
(not artificial) data. The suggested publication outlets in which to look for a suitable paper are:
• Management Science
• Operations Research
• INFORMS Journal on Computing
• INFORMS Journal on Applied Analytics
• Journal of Machine Learning Research
• European Journal of Operational Research
• ICDM (The IEEE International Conference on Data Mining)
• NeurlPS (Conference on Neural Information Processing Systems)
• KDD (ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
Note: if you would decide to select a paper from elsewhere, please ensure that it is of sufficiently high quality and
makes a novel contribution to the area.
Once you have found an appropriate paper, report the following in separate subsections:
• Title, authors and complete citation (e.g. journal name, volume/issue, year, …)
• The data mining problem considered
• The data mining techniques used
• The results reported
• A critical discussion of the model and results (assumptions made, shortcomings, limitations, …).
Make sure you demonstrate that you understand what the article is all about and are able to provide a critical
discussion.
Do not copy and paste from the article. Using Turnitin, this will be easily detected!
SEMESTER 3 2020/21
Nature of Assessment: This is a SUMMATIVE ASSESSMENT. See ‘Weighting’ section above for the percentage that this
assignment counts towards your final module mark.
Word Limit: +/-10% either side of the word count (see above) is deemed to be acceptable. Any text that exceeds an
additional 10% will not attract any marks. The relevant word count includes items such as cover page, executive
summary, title page, table of contents, tables, figures, in-text citations and section headings, if used. The relevant word
count excludes your list of references and any appendices at the end of your coursework submission.
You should always include the word count (from Microsoft Word, not Turnitin), at the end of your coursework
submission, before your list of references.
Title/Cover Page: You must include a title/ cover page that includes: your Student ID, Module Code, Assignment Title,
Word Count. This assignment will be marked anonymously, please ensure that your name does not appear on any part
of your assignment.
References: You should use the Harvard style to reference your assignment. The library provide guidance on how to
reference in the Harvard style and this is available from: http://library.soton.ac.uk/sash/referencing
Submission Deadline: Please note that the submission deadline for Southampton Business School is 16.00 for ALL
assessments.
Turnitin Submission: The assignment MUST be submitted electronically via Turnitin, which is accessed via the
individual module on Blackboard. Further guidance on submitting assignments is available on the Blackboard support
pages.
It is important that you allow enough time prior to the submission deadline to ensure your submission is processed
on time as all late submissions are subject to a late penalty. We would recommend you allow 30 minutes to upload
your work and check the submission has been processed and is correct. Please make sure you submit to the correct
assignment link.
You will know that your submission has completed successfully when you see a message stating ‘Congratulations –
your submission is complete…’. It is vital that you make a note of your Submission ID (Digital Receipt Number). This
is a unique receipt number for your submission, and is proof of successful submission. You may be required to
provide this number at a later date. We recommend that you take a screenshot of this page, or note the number
down on a piece of paper. You should also receive an email receipt containing this number, and the number can be
found after submitting by following this guide. This method of checking your submission is particularly useful in the
event that you don’t receive an email receipt.
You are allowed to test submit your assignment via Turnitin before the due date. You can use Turnitin to check your
assignment for plagiarism before you submit your final version. See “Viewing Your Originality Report” for guidance.
Please see the Module Leader/lecturer on your module if you would like advice on the Turnitin Originality report.
The last submission prior to the deadline will be treated as the final submission and will be the copy that is
assessed by the marker.
It is your responsibility to ensure that the version received by the deadline is the final version, resubmissions after
the deadline will not be accepted in any circumstances.
Important: If you have any problems during the submission process you should contact ServiceLine immediately by
email at Serviceline@soton.ac.uk or by phone on +44 (0)23 8059 5656.
Late Penalties: Further information on penalties for work submitted after the deadline can be found here.
Special Considerations: If you believe that illness or other circumstances have adversely affected your academic
performance, information regarding the regulations governing Special Considerations can be accessed via the
Calendar: http://www.calendar.soton.ac.uk/sectionIV/special-considerations.html
SEMESTER 3 2020/21
Extension Requests: : Extension requests along with supporting evidence should be submitted to the Student Office
as soon as possible before the submission date. Information regarding the regulations governing extension requests
can be accessed via the Calendar: http://www.calendar.soton.ac.uk/sectionIV/special-considerations.html
Academic Integrity Policy: Please note that you can access Academic Integrity Guidance for Students via the Quality
Handbook: http://www.southampton.ac.uk/quality/assessment/academic_integrity.page?. Please note any
suspected cases of Academic Integrity will be notified to the Academic Integrity Officer for investigation.
Feedback: Southampton Business School is committed to providing feedback within 4 weeks (University working days).
Once the marks are released and you have received your feedback, you can meet with your Module Leader / Module
Lecturer / Personal Academic Tutor to discuss the feedback within 4 weeks from the release of marks date. Any
additional arrangements for feedback are listed in the Module Profile.
Student Support: Study skills and language support for Southampton Business School students is available at:
http://www.sbsaob.soton.ac.uk/study-skills-and-language-support/.
学霸联盟
