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Table 4 Feature description bank marketing dataset

From: Selecting critical features for data classification based on machine learning methods

No

Feature

Value

1

Age

Numeric

2

Job

Type of job categorical: admin, unknown, unemployed, management, housemaid, entrepreneur, student, blue-collar, self-employed, retired, technician, services

3

Marital

Marital status categorical: married, divorced, single (note: divorced means divorced or widowed)

4

Education

Categorical: unknown, secondary, primary, tertiary

5

Default

Has credit in default? (binary: yes, no)

6

Balance

Average yearly balance, in euros (numeric)

7

Housing

Has housing loan? (binary: yes, no)

8

Loan

Has a personal loan? (binary: yes, no)

9

Contact

Contact communication type categorical: unknown, telephone, cellular

10

Day

Last contact day of the month (numeric)

11

Month

Last contact month of the year category: Jan, Feb, Mar,…, Nov, Dec

12

Duration

Last contact duration, in seconds (numeric)

13

Campaign

Number of contacts performed during this campaign and for this client (numeric)

14

Pdays

Number of days that passed by after the client was last contacted from a previous campaign (numeric, − 1 means the client was not previously contacted)

15

Previous

Number of contacts performed before this campaign and for this client (numeric)

16

Poutcome

The outcome of the previous marketing campaign categorical: unknown, other, failure, success

17

Y

Has the client subscribed a term deposit? (binary: yes, no)