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 a term deposit (variable `subscribed`).
Format
a data frame with 4,521 observations of 17 variables.
- age (numeric)
age
- job
type of job
- marital
marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed)
- education
educational level (categorical: "unknown","secondary","primary","tertiary")
- default
has credit in default? (binary: "yes","no")
- balance
average yearly balance, in euros (numeric)
- housing
has housing loan? (binary: "yes","no")
- loan
has personal loan? (binary: "yes","no")
- contact
contact communication type (categorical: "unknown","telephone","cellular")
- day
last contact day of the month (numeric)
- month
last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")
- duration
last contact duration, in seconds (numeric)
- 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, -1 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: "unknown","other","failure","success")
- subscribed
has the client subscribed a term deposit? (binary: "yes","no")