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.
Deze oefening maakt deel uit van de cursus
Credit Risk Modeling in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Create the scatter plot for age and amount
plt.scatter(____[____], ____[____], c='blue', alpha=0.5)
plt.xlabel("Person Age")
plt.ylabel("Loan Amount")
plt.____()