Credit Risk Modeling in Python
Michael Crabtree
Data Scientist, Ford Motor Company
Possible causes of outliers:
Possible causes of outliers:
Feature | Coefficient With Outliers | Coefficient Without Outliers |
---|---|---|
Interest Rate | 0.2 | 0.01 |
Employment Length | 0.5 | 0.6 |
Income | 0.6 | 0.75 |
pd.crosstab(cr_loan['person_home_ownership'], cr_loan['loan_status'],
values=cr_loan['loan_int_rate'], aggfunc='mean').round(2)
Detecting outliers visually
.drop()
method within Pandasindices = cr_loan[cr_loan['person_emp_length'] >= 60].index
cr_loan.drop(indices, inplace=True)
Credit Risk Modeling in Python