##**Archivos**

*   **train.csv** - El dataset de entrenamiento
*   **pred.csv** - El dataset que deben predecir
*   **ejemplo_predicciones.csv** - Un ejemplo del formato que deben tener los archivos de predicciones para ingresarlos a Kaggle

## **Columnas**

age (numeric)
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")
   # related with the last contact of the current campaign:
 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")

   # other attributes:
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")
   # social and economic context attributes
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)     
nr.employed: number of employees - quarterly indicator (numeric)