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 debtstudent: A factor with levels ‘No’ and ‘Yes’ indicating whether the customer is a studentbalance: The average balance that the customer has remaining on their credit card after making their monthly paymentincome: 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()
student
No 7056
Yes 2944
Name: count, dtype: int64