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Files Size Format Created Updated License Source
3 122kB arff 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 https://www.openml.org/d/9 Author: Jeffrey C. Schlimmer ([email protected]) Source: UCI - 1987 Please cite: 1985 Auto Imports Database This data set consists of three types of entities: (a) the specification of an auto in terms of read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
autos_arff 30kB arff (30kB)
autos 24kB csv (24kB) , json (109kB)
autos_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 28kB zip (28kB)

autos_arff  

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autos  

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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
normalized-losses 1 number (default)
make 2 string (default)
fuel-type 3 string (default)
aspiration 4 string (default)
num-of-doors 5 string (default)
body-style 6 string (default)
drive-wheels 7 string (default)
engine-location 8 string (default)
wheel-base 9 number (default)
length 10 number (default)
width 11 number (default)
height 12 number (default)
curb-weight 13 year (default)
engine-type 14 string (default)
num-of-cylinders 15 string (default)
engine-size 16 number (default)
fuel-system 17 string (default)
bore 18 number (default)
stroke 19 number (default)
compression-ratio 20 any (default)
horsepower 21 number (default)
peak-rpm 22 number (default)
city-mpg 23 number (default)
highway-mpg 24 number (default)
price 25 number (default)
symboling 26 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/autos
data info machine-learning/autos
tree machine-learning/autos
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/autos/datapackage.json | grep path

# Get resources

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

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

curl -L https://datahub.io/machine-learning/autos/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/autos/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/autos/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/autos/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/autos/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/9

Author: Jeffrey C. Schlimmer ([email protected])
Source: UCI - 1987
Please cite:

1985 Auto Imports Database
This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, © its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process “symboling”. A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.

The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc…), and represents the average loss per car per year.

Several of the attributes in the database could be used as a “class” attribute.

Sources:

  1. 1985 Model Import Car and Truck Specifications, 1985 Ward’s Automotive Yearbook.
  2. Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038
  3. Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

Past Usage:
Kibler,~D., Aha,~D.~W., & Albert,~M. (1989). Instance-based prediction of real-valued attributes. {it Computational Intelligence}, {it 5}, 51–57.

Attribute Information:

  1. symboling: -3, -2, -1, 0, 1, 2, 3.
  2. normalized-losses: continuous from 65 to 256.
  3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo
  4. fuel-type: diesel, gas.
  5. aspiration: std, turbo.
  6. num-of-doors: four, two.
  7. body-style: hardtop, wagon, sedan, hatchback, convertible.
  8. drive-wheels: 4wd, fwd, rwd.
  9. engine-location: front, rear.
  10. wheel-base: continuous from 86.6 120.9.
  11. length: continuous from 141.1 to 208.1.
  12. width: continuous from 60.3 to 72.3.
  13. height: continuous from 47.8 to 59.8.
  14. curb-weight: continuous from 1488 to 4066.
  15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
  16. num-of-cylinders: eight, five, four, six, three, twelve, two.
  17. engine-size: continuous from 61 to 326.
  18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
  19. bore: continuous from 2.54 to 3.94.
  20. stroke: continuous from 2.07 to 4.17.
  21. compression-ratio: continuous from 7 to 23.
  22. horsepower: continuous from 48 to 288.
  23. peak-rpm: continuous from 4150 to 6600.
  24. city-mpg: continuous from 13 to 49.
  25. highway-mpg: continuous from 16 to 54.
  26. price: continuous from 5118 to 45400.
Datapackage.json

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