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unknown (from Weka repository)

Dependencies

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arff slurped Weka

Attribute Types

Integer,Floating Point

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# Instances: 159 / # Attributes: 16
HDF5 (29.6 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

auto_price

Version mldata

0

Comment

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. The original data (from the UCI repository) (http://www.ics.uci.edu/~mlearn/MLSummary.html) has 205 instances described by 26 attributes : - 15 continuous - 1 integer - 10 nominal The following provides more information on these attributes:

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.

The original data also has some missing attribute values denoted by "?" :

Attribute #: Number of instances missing a value: 2. 41 6. 2 19. 4 20. 4 22. 2 23. 2 26. 4

I've changed the original data in the following way : - All instances with unknowns were removed giving 159 instances. - The goal variable is "price" - All nominal attributes (10) were removed.

Original source: UCI machine learning repository. (http://www.ics.uci.edu/~mlearn/MLSummary.html). Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html Characteristics: 159 cases; 14 continuous variables; 1 nominal vars..

Names

symboling,normalized-losses,wheel-base,length,width,height,curb-weight,engine-size,bore,stroke,

Types

1. nominal:-3,-2,-1,0,1,2,3
2. numeric
3. numeric
4. numeric
5. numeric
6. numeric
7. numeric
8. numeric
9. numeric
10. numeric

Data (first 10 data points)

symb...	norm...	whee...	length	width	height	curb...	engi...	bore	stroke	...
2	164	99.8	176.6	66.2	54.3	2337	109	3.19	3.4	...
2	164	99.4	176.6	66.4	54.3	2824	136	3.19	3.4	...
1	158	105.8	192.7	71.4	55.7	2844	136	3.19	3.4	...
1	158	105.8	192.7	71.4	55.9	3086	131	3.13	3.4	...
2	192	101.2	176.8	64.8	54.3	2395	108	3.5	2.8	...
0	192	101.2	176.8	64.8	54.3	2395	108	3.5	2.8	...
0	188	101.2	176.8	64.8	54.3	2710	164	3.31	3.19	...
0	188	101.2	176.8	64.8	54.3	2765	164	3.31	3.19	...
2	121	88.4	141.1	60.3	53.2	1488	61	2.91	3.03	...
1	98	94.5	155.9	63.6	52.0	1874	90	3.03	3.11	...
...	...	...	...	...	...	...	...	...	...	...

Description

A jarfile containing 30 regression datasets collected by Luis Torgo (regression-datasets.jar, 10,090,266 Bytes).

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revision 1

by mldata on 2010-11-06 09:57

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