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Banknote authentication

machine-learning

Files Size Format Created Updated License Source
3 162kB 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/1462 Author: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe) Source: UCI - 2012 Please cite: UCI Dataset about distinguishing genuine and forged banknotes. Data were extracted from images that were taken read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
banknote-authentication_arff 45kB arff (45kB)
banknote-authentication 45kB csv (45kB) , json (99kB)
banknote-authentication_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 83kB zip (83kB)

banknote-authentication_arff  

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

banknote-authentication  

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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 number (default)
V3 3 number (default)
V4 4 number (default)
Class 5 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/banknote-authentication
data info machine-learning/banknote-authentication
tree machine-learning/banknote-authentication
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/banknote-authentication/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/machine-learning/banknote-authentication/r/0.arff

curl -L https://datahub.io/machine-learning/banknote-authentication/r/1.csv

curl -L https://datahub.io/machine-learning/banknote-authentication/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/banknote-authentication/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/banknote-authentication/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/banknote-authentication/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/banknote-authentication/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/1462

Author: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe)
Source: UCI - 2012
Please cite: UCI

Dataset about distinguishing genuine and forged banknotes. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. A Wavelet Transform tool was used to extract features from these images.

Attribute Information

V1. variance of Wavelet Transformed image (continuous)
V2. skewness of Wavelet Transformed image (continuous)
V3. curtosis of Wavelet Transformed image (continuous)
V4. entropy of image (continuous)

Class (target). Presumably 1 for genuine and 2 for forged

Datapackage.json

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