Total expected loss
It's time to estimate the total expected loss given all your decisions. The data frame test_pred_df has the probability of default for each loan and that loan's value. Use these two values to calculate the expected loss for each loan. Then, you can sum those values and get the total expected loss.
For this exercise, you will assume that the exposure is the full value of the loan, and the loss given default is 100%. This means that a default on each the loan is a loss of the entire amount.
The data frame test_pred_df has been loaded into the workspace.
Cet exercice fait partie du cours
Credit Risk Modeling in Python
Instructions
- Print the top five rows of
test_pred_df. - Create a new column
expected_lossfor each loan by using the formula above. - Calculate the total expected loss of the entire portfolio, rounded to two decimal places, and store it as
tot_exp_loss. - Print the total expected loss.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Print the first five rows of the data frame
print(____.head())
# Calculate the bank's expected loss and assign it to a new column
____[____] = ____[____] * ____[____] * ____[____]
# Calculate the total expected loss to two decimal places
____ = round(np.____(____[____]),2)
# Print the total expected loss
print('Total expected loss: ', '${:,.2f}'.format(____))