Your task is to prepare the data, run an
analysis, answer research questions below and write a brief report with your
findings.
You are provided with personal loans data
from Brazil. There are two data sets – Accepted and Rejected. The last one does
not have an information about loan outcome as these people were rejected for
some subjective reasons. Also, there is a file with data description.
Brazil is a relatively poor country. One of
the major challenges in the assignment would be “a low quality” of borrowers in
Brazil. You should get models that are sensible and “good enough”.
1.
Review data and make decisions
what variables to include or exclude from the model. Report and explain your
decisions.
2.
Prepare Credit Scoring model.
Report decisions made during model development stage, final scorecard,
statistical summary of results, discuss quality of the model.
Hint: data set Rejected should have
Role “Score”. You can set it in Property menu.
3.
Create a separate “Machine
Learning” predictive model – logistic regression, decision tree, neural network,
whatever is available in SAS and works best. Alternatively, you can use any
other software. Compare its performance to Credit Scoring model. Discuss implications
of using each approach.
Hint: in SAS there is no need to
import the same data again, you can build a new connection between existing
data and some predictive model.
You must submit a formal report in MS Word
or PDF format. Your report will include:
1.
Introduction.
2.
Dataset description.
3.
Credit Scoring model with
related discussion.
4.
An alternative predictive model
with related discussion.
5.
Conclusion with comparison of
the above two approaches.
6.
Appendix with some extra
information, if required.
There is no requirement or limits for word
count. Your report should demonstrate completeness in covering all research
questions and brevity as no one loves
reading long reports. “A picture is worth a thousand words” – use data
visualisations to illustrate and support your research findings.
If you have any questions – feel free to
ask on the forum. You can discuss this exercise with me and other students. You
are encouraged to share ideas but not solutions. Remember about academic
integrity.
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