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Files Size Format Created Updated License Source
2 711MB csv zip 5 years ago 5 years ago Open Data Commons Public Domain Dedication and License
The resources for this dataset can be found at Author: Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi Source: Credit card fraud detection - Date 25th of June 2015 Please cite: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
creditcard 144MB csv (144MB) , json (212MB)
creditcard_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 210MB zip (210MB)


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Field information

Field Name Order Type (Format) Description
Time 1 number (default)
V1 2 number (default)
V2 3 number (default)
V3 4 number (default)
V4 5 number (default)
V5 6 number (default)
V6 7 number (default)
V7 8 number (default)
V8 9 number (default)
V9 10 number (default)
V10 11 number (default)
V11 12 number (default)
V12 13 number (default)
V13 14 number (default)
V14 15 number (default)
V15 16 number (default)
V16 17 number (default)
V17 18 number (default)
V18 19 number (default)
V19 20 number (default)
V20 21 number (default)
V21 22 number (default)
V22 23 number (default)
V23 24 number (default)
V24 25 number (default)
V25 26 number (default)
V26 27 number (default)
V27 28 number (default)
V28 29 number (default)
Amount 30 number (default)
Class 31 string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get
data info machine-learning/creditcard
tree machine-learning/creditcard
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

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

install.packages("jsonlite", repos="")

json_file <- ''
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

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

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 = ''

# 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('')

# print list of all resources:

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

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 = ''

// 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) {
  // 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
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data

Read me

The resources for this dataset can be found at

Author: Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi
Source: Credit card fraud detection - Date 25th of June 2015
Please cite: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are ‘Time’ and ‘Amount’. Feature ‘Time’ contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature ‘Amount’ is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature ‘Class’ is the response variable and it takes value 1 in case of fraud and 0 otherwise.

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group ( of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on and


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