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
Multivariate
Subject Area
Business
Associated Tasks
Classification
Feature Type
Categorical, Integer
# Instances
45211
# Features
16
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?
No
Introductory Paper
By Sérgio Moro, P. Cortez, P. Rita. 2014
Published in Decision Support Systems
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| age | Feature | Integer | Age | no | ||
| job | Feature | Categorical | Occupation | type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') | no | |
| marital | Feature | Categorical | Marital Status | marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) | no | |
| education | Feature | Categorical | Education Level | (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') | no | |
| default | Feature | Binary | has credit in default? | no | ||
| balance | Feature | Integer | average yearly balance | euros | no | |
| housing | Feature | Binary | has housing loan? | no | ||
| loan | Feature | Binary | has personal loan? | no | ||
| contact | Feature | Categorical | contact communication type (categorical: 'cellular','telephone') | yes | ||
| day_of_week | Feature | Date | last contact day of the week | no |
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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")
Dataset Files
| File | Size |
|---|---|
| bank.zip | 565.5 KB |
| bank-additional.zip | 434.2 KB |
Papers Citing this Dataset
Sort by Year, desc
By Suman Bera, Deeparnab Chakrabarty, Nicolas Flores, Maryam Negahbani. 2019
Published in ArXiv.
By Imtiaz Ziko, Eric Granger, Jing Yuan, Ismail Ayed. 2019
Published in ArXiv.
By Alexandre Lamy, Ziyuan Zhong, Aditya Menon, Nakul Verma. 2019
Published in ArXiv.
By Vasileios Iosifidis, Eirini Ntoutsi. 2019
Published in
By Afonso Vaz, Rafael Izbicki, Rafael Stern. 2018
Published in ArXiv.
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset bank_marketing = fetch_ucirepo(id=222) # data (as pandas dataframes) X = bank_marketing.data.features y = bank_marketing.data.targets # metadata print(bank_marketing.metadata) # variable information print(bank_marketing.variables)
Moro, S., Rita, P., & Cortez, P. (2014). Bank Marketing [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5K306.
Creators
S. Moro
P. Rita
P. Cortez
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.