This is the classic marketing bank dataset uploaded originally in the UCI Machine Learning Repository. The dataset gives you information about a marketing campaign of a financial institution in which you will have to analyze in order to find ways to look for future strategies in order to improve future marketing campaigns for the bank.

## Files

*   **train_bank_marketing.csv** - the training set
*   **test_bank_marketing.csv** - the test set
*   **sample_submission.csv** - a sample submission file in the correct format


## 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)
  
**Marketing-related 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")

**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 (i.e. treat them as "no" values).