bank_loans: Bank Loans
In sdcastillo/ExamPAData: Data Sets for Predictive Analytics Exam

bank_loans

R Documentation

Bank Loans

Description

Credit data from UCI Machine Learning Repository.

Usage

bank_loans

Format

data.frame, 41188 observations of 21 variables:

age: age (numeric).
job: type of job (categorical.
marital: marital status (categorical).
education: 'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown')
default: has credit in default? (categorical).
housing: has housing loan? (categorical).
loan: has personal loan? (categorical).
contact: contact communication type (categorical).
month: last contact month of year (categorical).
day_of_week: last contact day of the week (categorical).
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.
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).
emp.var.rate: employment variation rate.
cons.price.idx: consumer price index.
cons.conf.idx: consumer confidence index.
euribor3m: euribor 3 month rate.
nr.employed: number of employees.
y: has the client subscribed a term deposit?