Credit Card Default Data#

A simulated data set containing information on ten thousand customers. The aim here is to predict which customers will default on their credit card debt.

  • default: A factor with levels ‘No’ and ‘Yes’ indicating whether the customer defaulted on their debt

  • student: A factor with levels ‘No’ and ‘Yes’ indicating whether the customer is a student

  • balance: The average balance that the customer has remaining on their credit card after making their monthly payment

  • income: Income of customer

from ISLP import load_data
Default = load_data('Default')
Default.columns

Index(['default', 'student', 'balance', 'income'], dtype='object')

Default.shape

(10000, 4)

Default.columns

Index(['default', 'student', 'balance', 'income'], dtype='object')

Default.describe()
balance income
count 10000.000000 10000.000000
mean 835.374886 33516.981876
std 483.714985 13336.639563
min 0.000000 771.967729
25% 481.731105 21340.462903
50% 823.636973 34552.644802
75% 1166.308386 43807.729272
max 2654.322576 73554.233495 
Default['student'].value_counts()

No     7056
Yes    2944
Name: student, dtype: int64