Acceptance rate impact
Now, look at the loan_amnt
of each loan to understand the impact on the portfolio for the acceptance rates. You can use cross tables with calculated values, like the average loan amount, of the new set of loans X_test
. For this, you will multiply the number of each with an average loan_amnt
value.
When printing these values, try formatting them as currency so that the numbers look more realistic. After all, credit risk is all about money. This is accomplished with the following code:
pd.options.display.float_format = '${:,.2f}'.format
The predictions data frame test_pred_df
, which now includes the loan_amnt
column from X_test
, has been loaded in the workspace.
This exercise is part of the course
Credit Risk Modeling in Python
Exercise instructions
- Print the summary statistics of the
loan_amnt
column using.describe()
. - Calculate the average value of
loan_amnt
and store it asavg_loan
. - Set the formatting for
pandas
to'${:,.2f}'
- Print the cross table of the true loan status and predicted loan status multiplying each by
avg_loan
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print the statistics of the loan amount column
print(____[____].____())
# Store the average loan amount
____ = np.____(____[____])
# Set the formatting for currency, and print the cross tab
pd.options.display.float_format = ____.format
print(pd.____(____[____],
____[____]).apply(lambda x: x * ____, axis = 0))