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  5. Credit Risk Modeling in Python

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Exercise

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.

Instructions 1/2

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  • Create a scatter plot of person age on the x-axis and loan_amnt on the y-axis.