---
title: Bank Marketing Data Dictionary
format: html
jupyter: data622
---

## Source

This dataset originates from a study by Portuguese researchers aiming to 
predict the outcome of calls that took place as part of a bank marketing campaign
from 2008 to 2014:

[A data-driven approach to predict the success of bank telemarketing](https://www.semanticscholar.org/paper/cab86052882d126d43f72108c6cb41b295cc8a9e)
By Sérgio Moro, P. Cortez, P. Rita. 2014

This paper was published in the journal _Decision Support Systems_

Several versions of this dataset exist. Moro 2014 uses a private dataset with much 
richer features for their analysis. Two simpler versions have been published on 
the [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/222/bank+marketing), where they serve as benchmarks in the machine learning community. For 
this assignment, we are using the dataset in [bank_marketing_dataset.csv](https://github.com/georgehagstrom/DATA622Fall2026/blob/main/website/assignments/labs/labData/bank_marketing_dataset.csv).

This dataset combines some of the features found in the 'bank-full.csv' and 'bank-additional-full.csv' files on the UCI repo. In particular, it has all of the macroeconomic indicators present in 'bank-additional-full', the 'year', and the rest of the features are from
'bank-full.csv'. 

## Variables

| Variable | Role | Type | Description |
|----------|------|------|-------------|
| age | Feature | Integer | Client age in years |
| job | Feature | Categorical | Occupation type (admin, blue-collar, entrepreneur, housemaid, management, retired, self-employed, services, student, technician, unemployed, unknown) |
| marital | Feature | Categorical | Marital status (married, divorced, single). Divorced includes widowed |
| education | Feature | Categorical | Education level (primary, secondary, tertiary, unknown) |
| default | Feature | Binary | Has credit in default? |
| balance | Feature | Integer | Average yearly balance in euros |
| housing | Feature | Binary | Has housing loan? |
| loan | Feature | Binary | Has personal loan? |
| contact | Feature | Categorical | Contact communication type (cellular, telephone, unknown) |
| month | Feature | Categorical | Last contact month of year |
| day | Feature | Integer | Day of call |
| duration | Feature | Integer | Duration of marketing call |
| campaign | Feature | Integer | Number of contacts during this campaign for this client |
| pdays | Feature | Integer | Days since last contact from a previous campaign. -1 means not previously contacted |
| previous | Feature | Integer | Number of contacts before this campaign for this client |
| poutcome | Feature | Categorical | Outcome of previous marketing campaign (unknown, failure, other, success) |
| month_numeric | Feature | Integer | Month of the call |
| year     | Feature | Integer | Year of the call |
| euribor3m | Feature | Numeric | [Euribor 3-month rate — daily indicator](https://www.euribor-rates.eu/en/current-euribor-rates/2/euribor-rate-3-months/) |
| emp.var.rate | Feature | Numeric | Employment variation rate in Portugal, change in unemployment rate from one quarter to the next — quarterly indicator |
| nr.employed | Feature | Numeric | Number of employees in Portugal — quarterly indicator (thousands) |
| cons.price.idx | Feature | Numeric | Consumer price index in Portugal — monthly indicator |
| cons.conf.idx | Feature | Numeric | Consumer confidence index in Portugal — monthly indicator |
| y | Target | Binary | Has the client subscribed a term deposit? |