Bank Marketing Data Set

Source

This data set was obtained by downloading bank-additional-full.csv (contained in bank-additional.zip) from https://archive.ics.uci.edu/ml/datasets/Bank+Marketing.

The table contains 41,188 rows and 21 columns.

The path to this data set is pub.demo.mleg.uci.bankmarketing.

Input Variables

There are 20 columns in the table that provide information about each client, such as age, marital status, and education level. A subset of these are related to the last contact of the current campaign, such as the month and day of the week the last contact was made as well as the number of days since the client was last contacted in a previous campaign. There are 10 columns in the table that are categorial, meaning that they contain textual values that correspond to a particular category for a given variable.


Column Name	Description	Type
`age`	Age of the client	Numeric
`job`	Client's occupation	Categorial: `admin` `blue-collar` `entrepreneur` `housemaid` `management` `retired` `self-employed` `services` `student` `technician` `unemployed` `unknown`
`marital`	Marital status	Categorial: `divorced` `married` `single` `unknown` Note: `divorced` means divorced or widowed
`education`	Client's education level	Categorial: `basic.4y` `basic.6y` `basic.9y` `high.school` `illiterate` `professional.course` `university.degree` `unknown`
`default`	Indicates whether the client has credit in default	Categorial: `no` `yes` `unknown`
`housing`	Indicates whether the client has a housing loan	Categorial: `no` `yes` `unknown`
`loan`	Indicates whether the client as a personal loan	Categorial: `no` `yes` `unknown`
`contact`	Type of contact communication	Categorial: `cellular` `telephone`
`month`	Month that last contact was made	Categorial: `jan` `feb` `⋮` `dec`
`day_of_week`	Day that last contact was made	Categorial: `mon` `tue` `wed` `thu` `fri`
`duration`	Duration of last contact in seconds	Numeric 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 for this client (including last contact)	Numeric
`pdays`	Number of days since the client was last contacted in a previous campaign	Numeric Note: `999` means client was not previously contacted
`previous`	Number of contacts performed before this campaign for this client	Numeric
`poutcome`	Outcome of the previous marketing campaign	Categorial: `failure` `nonexistent` `success`
`empvarrate`	Employment variation rate (quarterly indicator) Note: This column was named `emp.var.rate` in the original data set.	Numeric
`conspriceidx`	Consumer price index (monthly indicator) Note: This column was named `cons.price.idx` in the original data set.	Numeric
`consconfidx`	Consumer confidence index (monthly indicator) Note: This column was named `cons.conf.idx` in the original data set.	Numeric
`euribor3m`	Euribor 3-month rate (daily indicator)	Numeric
`nremployed`	Number of employees (quarterly indicator) Note: This column was named `nr.employed` in the original data set.	Numeric

Output Variable

There is one column in the table that corresponds to our target value.


Column Name	Description	Type
`y`	Indicates whether the client has subscribed for a term deposit	Binary (`yes` or `no`)

Dummy Variables

Since we cannot use textual data in our analysis, categorial variables are coded as dummy variables. Each dummy variable represents one of the categories in the categorial columns.


Column Name	Description	Type
`yy`	Client subscribes for a term deposit `y`=`'yes'`	Boolean (`0` or `1`)
`hsng`	Client has a housing loan `housing`=`'yes'`	Boolean (`0` or `1`)
`h_unk`	Unknown if the client has a housing loan `housing`=`'unknown'`	Boolean (`0` or `1`)
`def`	Client has credit in default `default`=`'yes'`	Boolean (`0` or `1`)
`d_unk`	Unknown if the client has credit in default `default`=`'unknown'`	Boolean (`0` or `1`)
`loans`	Client has a personal loan `loan`=`'yes'`	Boolean (`0` or `1`)
`l_unk`	Client has a personal loan `loan`=`'unknown'`	Boolean (`0` or `1`)
`nonxst`	Previous outcome of marketing campaign is nonexistent `poutcome`=`'nonexistent'`	Boolean (`0` or `1`)
`succ`	Previous outcome of marketing campaign was a success `poutcome`=`'success'`	Boolean (`0` or `1`)
`blue`	Client occupation: blue-collar worker `job`=`'blue-collar'`	Boolean (`0` or `1`)
`tech`	Client occupation: technician `job`=`'technician'`	Boolean (`0` or `1`)
`j_unk`	Client occupation: unknown `job`=`'unknown'`	Boolean (`0` or `1`)
`svcs`	Client occupation: services `job`=`'services'`	Boolean (`0` or `1`)
`mgmt`	Client occupation: management `job`=`'management'`	Boolean (`0` or `1`)
`ret`	Client occupation: retired `job`=`'retired'`	Boolean (`0` or `1`)
`entr`	Client occupation: entrepreneur `job`=`'entrepreneur'`	Boolean (`0` or `1`)
`self`	Client occupation: self-employed `job`=`'self-employed'`	Boolean (`0` or `1`)
`maid`	Client occupation: housemaid `job`=`'housemaid'`	Boolean (`0` or `1`)
`unemp`	Client occupation: unemployed `job`=`'unemployed'`	Boolean (`0` or `1`)
`stud`	Client occupation: student `job`=`'student'`	Boolean (`0` or `1`)
`marr`	Marital status: married `marital`=`'married'`	Boolean (`0` or `1`)
`sgl`	Marital status: single `marital`=`'single'`	Boolean (`0` or `1`)
`m_unk`	Marital status: unknown `marital`=`'unknown'`	Boolean (`0` or `1`)