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Automobile Data Set
Download: Data Folder, Data Set Description

Abstract: From 1985 Ward's Automotive Yearbook

Data Set Characteristics:  

Multivariate

Number of Instances:

205

Area:

N/A

Attribute Characteristics:

Categorical, Integer, Real

Number of Attributes:

26

Date Donated

1987-05-19

Associated Tasks:

Regression

Missing Values?

Yes

Number of Web Hits:

168033


Source:

Creator/Donor:

Jeffrey C. Schlimmer (Jeffrey.Schlimmer '@' a.gp.cs.cmu.edu)

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


Data Set Information:

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, (c) 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.

Note: Several of the attributes in the database could be used as a "class" attribute.


Attribute Information:

Attribute: Attribute Range

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.


Relevant Papers:

Kibler, D., Aha, D.W., & Albert,M. (1989). Instance-based prediction of real-valued attributes. Computational Intelligence, Vol 5, 51--57.
[Web Link]


Papers That Cite This Data Set1:

Geraldine E. Rosario and Elke A. Rundensteiner and David C. Brown and Matthew O. Ward. Mapping Nominal Values to Numbers for Effective Visualization. INFOVIS. 2003. [View Context].

Wai Lam and Kin Keung and Charles X. Ling. PR 1527. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. 2001. [View Context].

Yongge Wang. A New Approach to Fitting Linear Models in High Dimensional Spaces. Alastair Scott (Department of Statistics, University of Auckland). [View Context].


Citation Request:

Please refer to the Machine Learning Repository's citation policy


[1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info

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