Automobile
Donated on 5/18/1987
From 1985 Ward's Automotive Yearbook
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Regression
Feature Type
Categorical, Integer, Real
# Instances
205
# Features
25
Dataset Information
Additional 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.
Has Missing Values?
Yes
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| price | Feature | Continuous | continuous from 5118 to 45400 | yes | |
| highway-mpg | Feature | Continuous | continuous from 16 to 54 | no | |
| city-mpg | Feature | Continuous | continuous from 13 to 49 | no | |
| peak-rpm | Feature | Continuous | continuous from 4150 to 6600 | yes | |
| horsepower | Feature | Continuous | continuous from 48 to 288 | yes | |
| compression-ratio | Feature | Continuous | continuous from 7 to 23 | no | |
| stroke | Feature | Continuous | continuous from 2.07 to 4.17 | yes | |
| bore | Feature | Continuous | continuous from 2.54 to 3.94 | yes | |
| fuel-system | Feature | Categorical | 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi | no | |
| engine-size | Feature | Continuous | continuous from 61 to 326 | no |
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Additional Variable 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.
Baseline Model Performance
Dataset Files
| File | Size |
|---|---|
| imports-85.data | 25.3 KB |
| imports-85.names | 4.6 KB |
| misc | 3.7 KB |
| app.css | 1.2 KB |
| Index | 144 Bytes |
Papers Citing this Dataset
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By Xin Cai, Guang Lin, Jinglai Li. 2019
Published in
By Haofan Zhang, Ke Nian, Thomas Coleman, Yuying Li. 2018
Published in International Journal of Data Science and Analytics.
By Fernando Otero, Alex Freitas. 2013
Published in GECCO '13.
By Matthew Medland, Fernando Otero. 2012
Published in GECCO '12.
By Muhammad Usman, Russel Pears. 2010
Published in Int. J. Adv. Comp. Techn..
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset automobile = fetch_ucirepo(id=10) # data (as pandas dataframes) X = automobile.data.features y = automobile.data.targets # metadata print(automobile.metadata) # variable information print(automobile.variables)
Schlimmer, J. (1985). Automobile [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5B01C.
Keywords
Creators
Jeffrey Schlimmer
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.