Theo Catterall/

Bank Marketing


Bank Marketing

This dataset consists of direct marketing campaigns by a Portuguese banking institution using phone calls. The campaigns aimed to sell subscriptions to a bank term deposit (see variable y).

Not sure where to begin? Scroll to the bottom to find challenges!

import pandas as pd

pd.read_csv("bank-marketing.csv", sep=";")

Data Dictionary

ageage of customer
jobtype of jobcategorical: "admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown"
maritalmarital statuscategorical: "divorced","married","single","unknown"; note: "divorced" means divorced or widowed
educationhighest degree of customercategorical: "basic.4y","basic.6y","basic.9y","","illiterate","professional.course","","unknown"
defaulthas credit in default?categorical: "no","yes","unknown"
housinghas housing loan?categorical: "no","yes","unknown"
loanhas personal loan?categorical: "no","yes","unknown"
contactcontact communication typecategorical: "cellular","telephone"
monthlast contact month of yearcategorical: "jan", "feb", "mar", ..., "nov", "dec"
day_of_weeklast contact day of the weekcategorical: "mon","tue","wed","thu","fri"
campaignnumber of contacts performed during this campaign and for this clientnumeric, includes last contact
pdaysnumber of days that passed by after the client was last contacted from a previous campaignnumeric; 999 means client was not previously contacted
previousnumber of contacts performed before this campaign and for this clientnumeric
poutcomeoutcome of the previous marketing campaigncategorical: "failure","nonexistent","success"
emp.var.rateemployment variation rate - quarterly indicatornumeric
cons.price.idxconsumer price index - monthly indicatornumeric
cons.conf.idxconsumer confidence index - monthly indicatornumeric
euribor3meuribor 3 month rate - daily indicatornumeric
nr.employednumber of employees - quarterly indicatornumeric
yhas the client subscribed a term deposit?binary: "yes","no"

Source of dataset.


  • 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
  • S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS.

Don't know where to start?

Challenges are brief tasks designed to help you practice specific skills:

  • 🗺️ Explore: What are the jobs of the people most likely to subscribe to a term deposit?
  • 📊 Visualize: Create a plot to visualize the number of people subscribing to a term deposit by month.
  • 🔎 Analyze: What impact does the number of contacts performed during the last campaign have on the likelihood that a customer subscribes to a term deposit?

Scenarios are broader questions to help you develop an end-to-end project for your portfolio:

You work for a financial services firm. The past few campaigns have not gone as well as the firm would have hoped, and they are looking for ways to optimize their marketing efforts.

They have supplied you with data from a previous campaign and some additional metrics such as the consumer price index and consumer confidence index. They want to know whether you can predict the likelihood of subscribing to a term deposit. The manager would also like to know what factors are most likely to increase a customer's probability of subscribing.

You will need to prepare a report that is accessible to a broad audience. It should outline your motivation, steps, findings, and conclusions.

✍️ If you have an idea for an interesting Scenario or Challenge, or have feedback on our existing ones, let us know! You can submit feedback by pressing the question mark in the top right corner of the screen and selecting "Give Feedback". Include the phrase "Content Feedback" to help us flag it in our system.

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