Citation Request:
  This dataset is publicly available for research. The details are described in [Moro et al., 2014]. 
  Please include this citation if you plan to use this database:

  [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, In press, http://dx.doi.org/10.1016/j.dss.2014.03.001

  Available at: [pdf] http://dx.doi.org/10.1016/j.dss.2014.03.001
                [bib] http://www3.dsi.uminho.pt/pcortez/bib/2014-dss.txt

1. Title: Bank Marketing (with social/economic context)

2. Sources
   Created by: Sérgio Moro (ISCTE-IUL), Paulo Cortez (Univ. Minho) and Paulo Rita (ISCTE-IUL) @ 2014
   
3. Past Usage:

  The full dataset (bank-additional-full.csv) was described and analyzed in:

  S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems (2014), doi:10.1016/j.dss.2014.03.001.
 
4. Relevant Information:

   This dataset is based on "Bank Marketing" UCI dataset (please check the description at: http://archive.ics.uci.edu/ml/datasets/Bank+Marketing).
   The data is enriched by the addition of five new social and economic features/attributes (national wide indicators from a ~10M population country), published by the Banco de Portugal and publicly available at: https://www.bportugal.pt/estatisticasweb.
   This dataset is almost identical to the one used in [Moro et al., 2014] (it does not include all attributes due to privacy concerns). 
   Using the rminer package and R tool (http://cran.r-project.org/web/packages/rminer/), we found that the addition of the five new social and economic attributes (made available here) lead to substantial improvement in the prediction of a success, even when the duration of the call is not included. Note: the file can be read in R using: d=read.table("bank-additional-full.csv",header=TRUE,sep=";")
   
   The zip file includes two datasets: 
      1) bank-additional-full.csv with all examples, ordered by date (from May 2008 to November 2010).
      2) bank-additional.csv with 10% of the examples (4119), randomly selected from bank-additional-full.csv.
   The smallest dataset is provided to test more computationally demanding machine learning algorithms (e.g., SVM).

   The binary classification goal is to predict if the client will subscribe a bank term deposit (variable y).

5. Number of Instances: 41188 for bank-additional-full.csv

6. Number of Attributes: 20 + output attribute.

7. Attribute information:

   For more information, read [Moro et al., 2014].

   Input variables:
   # bank client data:
   1 - age (numeric)
   2 - job : type of job (categorical: "admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown")
   3 - marital : marital status (categorical: "divorced","married","single","unknown"; note: "divorced" means divorced or widowed)
   4 - education (categorical: "basic.4y","basic.6y","basic.9y","high.school","illiterate","professional.course","university.degree","unknown")
   5 - default: has credit in default? (categorical: "no","yes","unknown")
   6 - housing: has housing loan? (categorical: "no","yes","unknown")
   7 - loan: has personal loan? (categorical: "no","yes","unknown")
   # related with the last contact of the current campaign:
   8 - contact: contact communication type (categorical: "cellular","telephone") 
   9 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")
  10 - day_of_week: last contact day of the week (categorical: "mon","tue","wed","thu","fri")
  11 - 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.
   # other attributes:
  12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
  13 - 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)
  14 - previous: number of contacts performed before this campaign and for this client (numeric)
  15 - poutcome: outcome of the previous marketing campaign (categorical: "failure","nonexistent","success")
   # social and economic context attributes
  16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)
  17 - cons.price.idx: consumer price index - monthly indicator (numeric)     
  18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)     
  19 - euribor3m: euribor 3 month rate - daily indicator (numeric)
  20 - nr.employed: number of employees - quarterly indicator (numeric)

  Output variable (desired target):
  21 - y - has the client subscribed a term deposit? (binary: "yes","no")

8. Missing Attribute Values: There are several missing values in some categorical attributes, all coded with the "unknown" label. These missing values can be treated as a possible class label or using deletion or imputation techniques.