Autos

Files	Size	Format	Created	Updated	License	Source
3	122kB	arff csv zip	4 years ago	4 years ago	Open Data Commons Public Domain Dedication and License

Files

Size

Format

Created

Updated

License

Source

122kB

arff csv zip

4 years ago

Open Data Commons Public Domain Dedication and License

Data Files

Download files in this dataset

File	Description	Size	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

Signup to Premium Service for additional or customised data - Get Started

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

autos

Signup to Premium Service for additional or customised data - Get Started

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

Field information

Field Name	Order	Type (Format)
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)

Not found what you're looking for?

You can search over a thousand datasets on datahub

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:

1985 Model Import Car and Truck Specifications, 1985 Ward’s Automotive Yearbook.
Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038
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:

symboling: -3, -2, -1, 0, 1, 2, 3.
normalized-losses: continuous from 65 to 256.
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
fuel-type: diesel, gas.
aspiration: std, turbo.
num-of-doors: four, two.
body-style: hardtop, wagon, sedan, hatchback, convertible.
drive-wheels: 4wd, fwd, rwd.
engine-location: front, rear.
wheel-base: continuous from 86.6 120.9.
length: continuous from 141.1 to 208.1.
width: continuous from 60.3 to 72.3.
height: continuous from 47.8 to 59.8.
curb-weight: continuous from 1488 to 4066.
engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
num-of-cylinders: eight, five, four, six, three, twelve, two.
engine-size: continuous from 61 to 326.
fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
bore: continuous from 2.54 to 3.94.
stroke: continuous from 2.07 to 4.17.
compression-ratio: continuous from 7 to 23.
horsepower: continuous from 48 to 288.
peak-rpm: continuous from 4150 to 6600.
city-mpg: continuous from 13 to 49.
highway-mpg: continuous from 16 to 54.
price: continuous from 5118 to 45400.