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Bank marketing

machine-learning

Files Size Format Created Updated License Source
3 6MB arff csv zip 10 months ago 10 months ago Open Data Commons Public Domain Dedication and License
The resources for this dataset can be found at https://www.openml.org/d/1461 Author: Paulo Cortez, Sérgio Moro Source: UCI Please cite: S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
bank-marketing_arff 3MB arff (3MB)
bank-marketing 4MB csv (4MB) , json (11MB)
bank-marketing_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 2MB zip (2MB)

bank-marketing_arff  

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This is a preview version. There might be more data in the original version.

bank-marketing  

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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
V1 1 number (default)
V2 2 string (default)
V3 3 string (default)
V4 4 string (default)
V5 5 string (default)
V6 6 number (default)
V7 7 string (default)
V8 8 string (default)
V9 9 string (default)
V10 10 number (default)
V11 11 string (default)
V12 12 number (default)
V13 13 number (default)
V14 14 number (default)
V15 15 number (default)
V16 16 string (default)
Class 17 number (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/machine-learning/bank-marketing
data info machine-learning/bank-marketing
tree machine-learning/bank-marketing
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/bank-marketing/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/machine-learning/bank-marketing/r/0.arff

curl -L https://datahub.io/machine-learning/bank-marketing/r/1.csv

curl -L https://datahub.io/machine-learning/bank-marketing/r/2.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/machine-learning/bank-marketing/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/machine-learning/bank-marketing/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/machine-learning/bank-marketing/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/machine-learning/bank-marketing/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

The resources for this dataset can be found at https://www.openml.org/d/1461

Author: Paulo Cortez, Sérgio Moro Source: UCI Please cite: S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM’2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.

Bank Marketing
The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.

The classification goal is to predict if the client will subscribe a term deposit (variable y).

Attribute information

For more information, read [Moro et al., 2011].

Input variables:

  • bank client data:

1 - age (numeric)

2 - job : type of job (categorical: “admin.”,“unknown”,“unemployed”,“management”,“housemaid”,“entrepreneur”, “student”,“blue-collar”,“self-employed”,“retired”,“technician”,“services”)

3 - marital : marital status (categorical: “married”,“divorced”,“single”; note: “divorced” means divorced or widowed)

4 - education (categorical: “unknown”,“secondary”,“primary”,“tertiary”)

5 - default: has credit in default? (binary: “yes”,“no”)

6 - balance: average yearly balance, in euros (numeric)

7 - housing: has housing loan? (binary: “yes”,“no”)

8 - loan: has personal loan? (binary: “yes”,“no”)

  • related with the last contact of the current campaign:

9 - contact: contact communication type (categorical: “unknown”,“telephone”,“cellular”)

10 - day: last contact day of the month (numeric)

11 - month: last contact month of year (categorical: “jan”, “feb”, “mar”, …, “nov”, “dec”)

12 - duration: last contact duration, in seconds (numeric)

  • other attributes:

13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)

14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)

15 - previous: number of contacts performed before this campaign and for this client (numeric)

16 - poutcome: outcome of the previous marketing campaign (categorical: “unknown”,“other”,“failure”,“success”)

  • output variable (desired target):

17 - y - has the client subscribed a term deposit? (binary: “yes”,“no”)

Datapackage.json

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