Bank marketing

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe to a term deposit (variable y).

This dataset is used in the tutorial Buy or not / Predict from tabular data.

Dataset origin

This dataset comes from the UC Irvine Machine Learning Repository, more specifically, the Bank Marketing Data Set from:
[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

Attribute information

Output variable (desired target)

  • purchased - has the client subscribed to a term deposit? (binary: yes, no)

Input variables

bank client data

  • age (numeric)

  • job : type of job (categorical: admin., blue-collar, entrepreneur, housemaid, management, retired, self-employed, services, student, technician, unemployed, 0unknown)

  • marital : marital status (categorical: divorced, married, single, unknown; note: divorced means divorced or widowed)

  • education (categorical: basic.4y, basic.6y, basic.9y,, illiterate, professional.course,, unknown)

  • default: has credit in default? (categorical: no, yes, unknown)

  • housing: has housing loan? (categorical: no, yes, unknown)

  • loan: has personal loan? (categorical: no, yes, unknown)

related with the last contact of the current campaign

  • contact: contact communication type (categorical: cellular, telephone)

  • month: last contact month of year (categorical: jan, feb, mar, …​, nov, dec)

  • day_of_week: last contact day of the week (categorical: mon, tue, wed, thu, fri)

  • duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then purchased=no). Yet, the duration is not known before a call is performed. Also, after the end of the call purchased 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

  • campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)

  • 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)

  • previous: number of contacts performed before this campaign and for this client (numeric)

  • poutcome: outcome of the previous marketing campaign (categorical: failure,nonexistent,success)

social and economic context attributes

  • emp.var.rate: employment variation rate - quarterly indicator (numeric)

  • cons.price.idx: consumer price index - monthly indicator (numeric)

  • cons.conf.idx: consumer confidence index - monthly indicator (numeric)

  • euribor3m: euribor 3 month rate - daily indicator (numeric)

  • nr.employed: number of employees - quarterly indicator (numeric)

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