Bank Marketing

Donated on 2/13/2012

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 y).

Dataset Characteristics


Subject Area


Associated Tasks


Feature Type

Categorical, Integer

# Instances


# Features


Dataset Information

Additional Information

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Has Missing Values?


Introductory Paper

A data-driven approach to predict the success of bank telemarketing

By Sérgio Moro, P. Cortez, P. Rita. 2014

Published in Decision Support Systems

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
jobFeatureCategoricalOccupationtype of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')no
maritalFeatureCategoricalMarital Statusmarital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)no
educationFeatureCategoricalEducation Level(categorical: 'basic.4y','basic.6y','basic.9y','','illiterate','professional.course','','unknown')no
defaultFeatureBinaryhas credit in default?no
balanceFeatureIntegeraverage yearly balanceeurosno
housingFeatureBinaryhas housing loan?no
loanFeatureBinaryhas personal loan?no
contactFeatureCategoricalcontact communication type (categorical: 'cellular','telephone') yes
day_of_weekFeatureDatelast contact day of the weekno

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Additional Variable Information

Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - 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) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no")

Papers Citing this Dataset

Fair Algorithms for Clustering

By Suman Bera, Deeparnab Chakrabarty, Nicolas Flores, Maryam Negahbani. 2019

Published in ArXiv.

Clustering with Fairness Constraints: A Flexible and Scalable Approach

By Imtiaz Ziko, Eric Granger, Jing Yuan, Ismail Ayed. 2019

Published in ArXiv.

Noise-tolerant fair classification

By Alexandre Lamy, Ziyuan Zhong, Aditya Menon, Nakul Verma. 2019

Published in ArXiv.

AdaFair: Cumulative Fairness Adaptive Boosting

By Vasileios Iosifidis, Eirini Ntoutsi. 2019

Published in

Quantification under prior probability shift: the ratio estimator and its extensions

By Afonso Vaz, Rafael Izbicki, Rafael Stern. 2018

Published in ArXiv.

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9 citations


S. Moro

P. Rita

P. Cortez


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