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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

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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.____()
Edit and Run Code