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Gender field containing values

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Reference no: EM133147497

You have the following meta data:

a. 100000 records

b. Gender field containing values either 'M' or 'F' or missing(50000 records have missing gender)

c. Credit score with values ranging from 600 to 900

d. Address which contains postal code(missing values comprise 30%)

e. Income ranging from 25K to 100K with no missing values

What are 5 derived variables or 5 features that could be engineered

Reference no: EM133147497

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