## File descriptions

* **train.csv** - the training set
* **test.csv** - the test set
* **sample_submission.csv** - a sample submission in the correct format.

## Data fields

### Input attributes:

* **age** - age of client.
* **job** - type of job (categorical: `admin`, `blue-collar`, `entrepreneur`, `housemaid`, `management`, `retired`, `self-employed`, `services`, `student`, `technician`, `unemployed`, `unknown`)
* **marital** - marital status (categorical: `divorced`, `married`, `single`,`unknown`; note: `divorced` means divorced or widowed)
* **education** - (categorical: `basic.4y`,`basic.6y`,`basic.9y`,`high.school`,`illiterate`,`professional.course`,`university.degree`,`unknown`)
* **default** - has credit in default? (categorical: `no`,`yes`,`unknown`)
* **housing** - has housing loan? (categorical: `no`,`yes`,`unknown`)
* **loan** - has personal loan? (categorical: `no`,`yes`,`unknown`)
* **contact** - contact communication type (categorical: `cellular`,`telephone`) 
* **month** - last contact month of year (categorical: `jan`, `feb`, `mar`, ..., `nov`, `dec`)
* **day_of_week** - last contact day of the week (categorical: `mon`,`tue`,`wed`,`thu`,`fri`)
* **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: `failure`,`nonexistent`,`success`)
* **emp.var.rate** - employment variation rate - quarterly indicator (numeric)
* **cons.price.idx** - consumer price index - monthly indicator (numeric) 
* **cons.conf.idx** - consumer confidence index - monthly indicator (numeric) 
* **euribor3m** - euribor 3 month rate - daily indicator (numeric)
* **nr.employed** number of employees - quarterly indicator (numeric)

## Output variable:

`target` - has the client subscribed a term deposit? (binary: 1 or 0)