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) |