Visualizing loan approval yield
In the realm of financial services, understanding the factors that influence loan approval rates is crucial for both lenders and borrowers. A financial institution has conducted a study and collected data on loan applications, detailing the amount requested, the applicant's credit score, employment status, and the ultimate yield of the approval process. This rich dataset offers a window into the nuanced dynamics at play in loan decision-making. You have been asked to dive into the loan_approval_yield dataset to understand how loan amounts and credit scores influence approval yields.
The loan_approval_yield DataFrame, seaborn as sns, and matplotlib.pyplot as plt have been loaded for you.
Den här övningen är en del av kursen
Experimental Design in Python
Interaktiv övning med praktiskt arbete
Testa den här övningen genom att slutföra den här exempelkoden.
# Use Seaborn to create the bar graph
sns.catplot(x="____",
y="____",
hue="____",
kind="____",
data=loan_approval_yield)
plt.title("Loan Approval Yield by Amount and Credit Score")
plt.show()