Attribute Information:

Variables regarding bank client data:
1 - age (numeric)
2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')
3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
5 - default: has credit in default? (categorical: 'no','yes','unknown')
6 - housing: has housing loan? (categorical: 'no','yes','unknown')
7 - loan: has personal loan? (categorical: 'no','yes','unknown')
17 - balance: the individual's checking account balance

Variables related to macroeconomic factors:
8 - emp.var.rate: employment variation rate - quarterly indicator (numeric)
9 - cons.price.inx: consumer price index - monthly indicator (numeric) 
10 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 
11 - euribor3m: euribor 3 month rate - daily indicator (numeric)
12 - nr.employed: number of employees - quarterly indicator (numeric)

Other attributes:
13 - y - please ignore this column

Output variable (desired target):
16 - next.product: what is the next product that the consumer encountered, if any, in six months following data collection.

Source:
[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014