Visualizing credit outliers
You discovered outliers in person_emp_length where values greater than 60 were far above the norm. person_age is another column in which a person can use a common sense approach to say it is very unlikely that a person applying for a loan will be over 100 years old.
Visualizing the data here can be another easy way to detect outliers. You can use other numeric columns like loan_amnt and loan_int_rate to create plots with person_age to search for outliers.
The data set cr_loan has been loaded in the workspace.
This exercise is part of the course
Credit Risk Modeling in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create the scatter plot for age and amount
plt.scatter(____[____], ____[____], c='blue', alpha=0.5)
plt.xlabel("Person Age")
plt.ylabel("Loan Amount")
plt.____()