Credit Card Balance Data

Contents

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
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