COMP0050 Assignment
Data
Download from moodle the file COMP0050CourseworkData.zip.
This contains two datasets:
1) bankPortfolios.csv: The data contain information about assets held
by 7783 US commercial banks in their balance sheet in the 4th
quarter of 2007. These were collected from the Wharton Research
Data Services. Each row of the file is associated with a bank. The first
14 columns represent investments in the following asset classes.



Column 15 contains banks debt. Finally, column 16 contains a binary
variable corresponding to the output variable y that you need to predict (1
denotes default, 0 no default). Information on defaults comes from the list
of bank failures from the Federal Deposit Insurance Corporation (FBL-
FDIC) for the period 1/1/2008 - 7/1/2011.

2) 48_Industry_Portfolios_daily.csv: this dataset comes from Ken
French’s website
(http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.
html) and contains daily equity returns for 48 industries in the U.S.
Note that in the spreadsheet you can find two sets of data that
correspond to different ways of computing industrial averages (either
weighted equally or by market cap). You are free to select the version
you prefer.


Tasks
There will be two tasks corresponding to the two datasets:
1. The task is to build a model to predict whether a bank will default.
You should compare the performance of different methods (e.g.
logistic regression, classification trees/forests) in terms of their ability
to correctly predict defaults. You are free to focus on a subset of the
data (e.g. a reduced set of features, or a subset of banks) and to
manipulate the data as you like, but you should explain your
rationale. You should address in your analysis the issue of
unbalanced data.

2. Focus on a subset of the data and perform a clustering analysis on
the daily equity return data. Can you find meaningful interpretations of
the clusters? Do certain industries tend to cluster together?
For both tasks, justify whether you want to focus only on subsamples of the
data. You are also free to explore questions related to the data and the
tasks you think are interesting, as long as your analysis includes the
development of predictive models of defaults for what concerns task 1 and