Credit Card Balance Data

A simulated data set containing information on 400 customers.

• Income: Income in $1,000’s

• Limit: Credit limit

• Rating: Credit rating

• Cards: Number of credit cards

• Age: Age in years

• Education: Education in years

• Own: A factor with levels No and Yes indicating whether the individual owns a home

• Student: A factor with levels No and Yes indicating whether the individual is a student

• Married: A factor with levels No and Yes indicating whether the individual is married

• Region: A factor with levels East, South, and West indicating the individual’s geographical location

• Balance: Average credit card balance in $

Source

Simulated data. Many thanks to Albert Kim for helpful suggestions, and for supplying a draft of the man documentation page on Oct 19, 2017.

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

Index(['ID', 'Income', 'Limit', 'Rating', 'Cards', 'Age', 'Education',
       'Gender', 'Student', 'Married', 'Ethnicity', 'Balance'],
      dtype='object')

Credit.shape

(400, 12)

Credit.columns

Index(['ID', 'Income', 'Limit', 'Rating', 'Cards', 'Age', 'Education',
       'Gender', 'Student', 'Married', 'Ethnicity', 'Balance'],
      dtype='object')

Credit.describe().iloc[:,:4]

	ID	Income	Limit	Rating
count	400.000000	400.000000	400.000000	400.000000
mean	200.500000	45.218885	4735.600000	354.940000
std	115.614301	35.244273	2308.198848	154.724143
min	1.000000	10.354000	855.000000	93.000000
25%	100.750000	21.007250	3088.000000	247.250000
50%	200.500000	33.115500	4622.500000	344.000000
75%	300.250000	57.470750	5872.750000	437.250000
max	400.000000	186.634000	13913.000000	982.000000