bank marketing datasets




# 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”,””,”illiterate”,”professional.course”,””,”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”)
# related with the last contact of the current campaign:
8 – contact: contact communication type (categorical: “cellular”,”telephone”)
9 – month: last contact month of year (categorical: “jan”, “feb”, “mar”, …, “nov”, “dec”)
10 – day_of_week: last contact day of the week (categorical: “mon”,”tue”,”wed”,”thu”,”fri”)
11 – duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y=”no”). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
# other attributes:
12 – campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
13 – pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
14 – previous: number of contacts performed before this campaign and for this client (numeric)
15 – poutcome: outcome of the previous marketing campaign (categorical: “failure”,”nonexistent”,”success”)
# social and economic context attributes
16 – emp.var.rate: employment variation rate – quarterly indicator (numeric)
17 – cons.price.idx: consumer price index – monthly indicator (numeric)
18 – cons.conf.idx: consumer confidence index – monthly indicator (numeric)
19 – euribor3m: euribor 3 month rate – daily indicator (numeric)
20 – nr.employed: number of employees – quarterly indicator (numeric)

Output variable (desired target):
21 – y – has the client subscribed a term deposit? (binary: “yes”,”no”)