" !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! All nominal attributes and instances with missing values are deleted. Price treated as the class attribute. As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction using instance-based learning with encoding length selection. In Progress in Connectionist-Based Information Systems. Singapore: Springer-Verlag. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1. Title: 1985 Auto Imports Database 2. Source Information: -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 19 May 1987 -- 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 3. Past Usage: -- Kibler,~D., Aha,~D.~W., \\& Albert,~M. (1989). Instance-based prediction of real-valued attributes. {\\it Computational Intelligence}, {\\it 5}, 51--57. -- Predicted price of car using all numeric and Boolean attributes -- Method: an instance-based learning (IBL) algorithm derived from a localized k-nearest neighbor algorithm. Compared with a linear regression prediction...so all instances with missing attribute values were discarded. This resulted with a training set of 159 instances, which was also used as a test set (minus the actual instance during testing). -- Results: Percent Average Deviation Error of Prediction from Actual -- 11.84% for the IBL algorithm -- 14.12% for the resulting linear regression equation 4. Relevant Information: -- Description 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. 5. Number of Instances: 205 6. Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal 7. 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. 8. 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% " "0" "'autoPrice.names'" 99.8 99.4 105.8 105.8 101.2 101.2 101.2 101.2 88.4 94.5 94.5 93.7 93.7 93.7 93.7 93.7 93.7 103.3 95.9 86.6 86.6 93.7 93.7 93.7 96.5 96.5 96.5 96.5 96.5 96.5 96.5 96.5 113 93.1 93.1 93.1 93.1 93.1 98.8 98.8 98.8 98.8 98.8 104.9 110 110 106.7 115.6 96.6 93.7 93.7 93.7 93 96.3 96.3 96.3 96.3 96.3 96.3 94.5 94.5 94.5 94.5 94.5 94.5 94.5 94.5 94.5 95.1 97.2 97.2 100.4 100.4 100.4 91.3 91.3 99.2 107.9 107.9 107.9 107.9 107.9 107.9 108 93.7 93.7 93.7 93.7 93.7 103.3 94.5 99.1 99.1 99.1 99.1 99.1 99.1 93.7 93.7 93.3 97.2 97.2 97.2 97 97 97 97 96.9 96.9 95.7 95.7 95.7 95.7 95.7 95.7 95.7 95.7 95.7 95.7 95.7 95.7 95.7 94.5 94.5 94.5 94.5 98.4 98.4 98.4 98.4 98.4 98.4 102.4 102.4 102.4 102.4 102.4 102.9 102.9 104.5 97.3 97.3 97.3 97.3 97.3 97.3 97.3 94.5 104.3 104.3 104.3 104.3 104.3 104.3 109.1 109.1 109.1 109.1 109.1 176.6 176.6 192.7 192.7 176.8 176.8 176.8 176.8 141.1 155.9 158.8 157.3 157.3 157.3 157.3 157.3 157.3 174.6 173.2 144.6 144.6 150 150 150 163.4 157.1 167.5 167.5 175.4 175.4 175.4 169.1 199.6 159.1 159.1 159.1 166.8 166.8 177.8 177.8 177.8 177.8 177.8 175 190.9 190.9 187.5 202.6 180.3 157.3 157.3 157.3 157.3 173 173 172.4 172.4 172.4 172.4 165.3 165.3 165.3 165.3 170.2 165.3 165.6 165.3 170.2 162.4 173.4 173.4 181.7 184.6 184.6 170.7 170.7 178.5 186.7 186.7 186.7 186.7 186.7 186.7 186.7 157.3 157.3 157.3 167.3 167.3 174.6 168.9 186.6 186.6 186.6 186.6 186.6 186.6 156.9 157.9 157.3 172 172 172 172 172 173.5 173.5 173.6 173.6 158.7 158.7 158.7 169.7 169.7 169.7 166.3 166.3 166.3 166.3 166.3 166.3 166.3 168.7 168.7 168.7 168.7 176.2 176.2 176.2 176.2 176.2 176.2 175.6 175.6 175.6 175.6 175.6 183.5 183.5 187.8 171.7 171.7 171.7 171.7 171.7 171.7 171.7 165.7 188.8 188.8 188.8 188.8 188.8 188.8 188.8 188.8 188.8 188.8 188.8 66.2 66.4 71.4 71.4 64.8 64.8 64.8 64.8 60.3 63.6 63.6 63.8 63.8 63.8 63.8 63.8 63.8 64.6 66.3 63.9 63.9 64 64 64 64 63.9 65.2 65.2 65.2 62.5 65.2 66 69.6 64.2 64.2 64.2 64.2 64.2 66.5 66.5 66.5 66.5 66.5 66.1 70.3 70.3 70.3 71.7 70.5 64.4 64.4 64.4 63.8 65.4 65.4 65.4 65.4 65.4 65.4 63.8 63.8 63.8 63.8 63.8 63.8 63.8 63.8 63.8 63.8 65.2 65.2 66.5 66.5 66.5 67.9 67.9 67.9 68.4 68.4 68.4 68.4 68.4 68.4 68.3 63.8 63.8 63.8 63.8 63.8 64.6 68.3 66.5 66.5 66.5 66.5 66.5 66.5 63.4 63.6 63.8 65.4 65.4 65.4 65.4 65.4 65.4 65.4 65.4 65.4 63.6 63.6 63.6 63.6 63.6 63.6 64.4 64.4 64.4 64.4 64.4 64.4 64.4 64 64 64 64 65.6 65.6 65.6 65.6 65.6 65.6 66.5 66.5 66.5 66.5 66.5 67.7 67.7 66.5 65.5 65.5 65.5 65.5 65.5 65.5 65.5 64 67.2 67.2 67.2 67.2 67.2 67.2 68.9 68.8 68.9 68.9 68.9 54.3 54.3 55.7 55.9 54.3 54.3 54.3 54.3 53.2 52 52 50.8 50.8 50.8 50.6 50.6 50.6 59.8 50.2 50.8 50.8 52.6 52.6 52.6 54.5 58.3 53.3 53.3 54.1 54.1 54.1 51 52.8 54.1 54.1 54.1 54.1 54.1 53.7 55.5 53.7 55.5 55.5 54.4 56.5 58.7 54.9 56.3 50.8 50.8 50.8 50.8 50.8 49.4 49.4 51.6 51.6 51.6 51.6 54.5 54.5 54.5 54.5 53.5 54.5 53.3 54.5 53.5 53.3 54.7 54.7 55.1 56.1 55.1 49.7 49.7 49.7 56.7 56.7 56.7 56.7 56.7 56.7 56 50.8 50.8 50.6 50.8 50.8 59.8 50.2 56.1 56.1 56.1 56.1 56.1 56.1 53.7 53.7 55.7 52.5 52.5 52.5 54.3 54.3 53 53 54.9 54.9 54.5 54.5 54.5 59.1 59.1 59.1 53 52.8 53 52.8 53 52.8 52.8 52.6 52.6 52.6 52.6 52 52 52 52 52 53 54.9 54.9 53.9 54.9 53.9 52 52 54.1 55.7 55.7 55.7 55.7 55.7 55.7 55.7 51.4 56.2 57.5 56.2 57.5 56.2 57.5 55.5 55.5 55.5 55.5 55.5 3.19 3.19 3.19 3.13 3.5 3.5 3.31 3.31 2.91 3.03 3.03 2.97 2.97 3.03 2.97 2.97 2.97 3.34 3.6 2.91 2.91 2.91 2.91 2.91 2.91 2.92 3.15 3.15 3.15 3.15 3.15 3.15 3.63 3.03 3.03 3.03 3.03 3.08 3.39 3.39 3.39 3.39 3.39 3.76 3.58 3.58 3.58 3.58 3.46 2.97 2.97 2.97 3.03 3.17 3.35 3.35 3.35 3.17 3.17 3.15 2.99 3.15 3.15 3.15 3.15 3.15 3.15 3.15 3.15 3.33 3.33 3.43 3.43 3.43 3.43 3.43 3.43 3.46 3.7 3.46 3.7 3.46 3.7 3.61 2.97 3.03 2.97 2.97 2.97 3.35 3.94 3.54 3.54 2.54 3.54 3.54 3.54 3.62 3.62 3.62 3.62 3.62 3.62 3.62 3.62 3.62 3.62 3.62 3.62 3.05 3.05 3.05 3.05 3.05 3.05 3.19 3.19 3.27 3.27 3.19 3.19 3.19 3.19 3.19 3.24 3.24 3.62 3.62 3.62 3.62 3.62 3.62 3.31 3.27 3.31 3.31 3.31 3.27 3.27 3.27 3.01 3.19 3.01 3.19 3.19 3.01 3.19 3.19 3.78 3.78 3.78 3.78 3.62 3.62 3.78 3.78 3.58 3.01 3.78 3.4 3.4 3.4 3.4 2.8 2.8 3.19 3.19 3.03 3.11 3.11 3.23 3.23 3.39 3.23 3.23 3.23 3.46 3.9 3.41 3.41 3.07 3.41 3.41 3.41 3.41 3.58 3.58 3.58 3.58 3.58 3.58 4.17 3.15 3.15 3.15 3.15 3.15 3.39 3.39 3.39 3.39 3.39 3.16 3.64 3.64 3.64 3.64 3.1 3.23 3.23 3.23 3.39 3.46 3.46 3.46 3.46 3.46 3.46 3.29 3.47 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.47 3.47 3.27 3.27 3.27 3.27 3.27 3.27 3.19 3.52 2.19 3.52 3.19 3.52 3.21 3.23 3.39 3.23 3.23 3.23 3.46 3.11 3.07 3.07 2.07 3.07 3.07 3.07 2.36 2.64 2.64 2.64 2.64 2.64 2.64 2.64 2.64 2.64 2.64 2.64 3.03 3.03 3.03 3.03 3.03 3.03 3.03 3.03 3.35 3.35 3.03 3.03 3.03 3.03 3.03 3.08 3.08 3.5 3.5 3.5 3.5 3.5 3.5 3.54 3.35 3.54 3.54 3.54 3.35 3.35 3.35 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.15 3.15 3.15 3.15 3.15 3.15 3.15 3.15 2.87 3.4 3.15 10 8 8.5 8.3 8.8 8.8 9 9 9.5 9.6 9.6 9.41 9.4 7.6 9.4 9.4 9.4 8.5 7 9.6 9.2 10.1 9.2 9.2 9.2 9.2 9 9 9 9 9 9.1 8.1 9 9 9 9 9 8.6 8.6 8.6 8.6 8.6 8 21.5 21.5 21.5 21.5 8.3 9.4 9.4 9.4 7.6 7.5 8.5 8.5 8.5 7.5 7.5 9.4 21.9 9.4 9.4 9.4 9.4 9.4 9.4 9.4 9.4 8.5 8.5 9 9 9 9 7.8 9 8.4 21 8.4 21 8.4 21 7 9.4 7.6 9.4 9.4 9.4 8.5 9.5 9.31 9.3 9.3 9.3 9 9 9 8.7 8.7 9.5 9.5 9 9 7.7 9 9 9 7.7 9 9 9 9 9 9 9 9 22.5 22.5 9 9 9 9 9 9.4 9.4 9.3 9.3 9.3 9.3 9.3 9.3 8.7 22.5 8.7 8.7 8.7 9.3 9.3 9.2 23 9 23 9 9 23 10 8.5 9.5 9.5 9.5 9.5 7.5 7.5 9.5 8.7 8.8 23 9.5 2 2 1 1 2 0 0 0 2 1 0 1 1 1 1 1 1 -1 3 2 2 1 1 1 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 0 -1 -1 0 -1 3 2 2 2 1 3 3 1 1 1 -1 1 1 1 1 1 1 1 1 1 2 0 0 0 0 0 3 3 1 0 0 0 0 0 0 0 1 1 1 1 1 -1 3 3 2 3 2 3 2 2 2 2 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 2 2 2 2 2 -1 -1 -1 -1 -1 3 3 -1 2 2 2 2 2 2 2 3 -2 -1 -2 -1 -2 -1 -1 -1 -1 -1 -1 164 164 158 158 192 192 188 188 121 98 81 118 118 118 148 148 148 110 145 137 137 101 101 101 110 78 106 106 85 85 85 107 145 104 104 104 113 113 129 115 129 115 115 118 93 93 93 93 142 161 161 161 161 153 153 125 125 125 137 128 128 128 122 103 128 128 122 103 168 106 106 128 108 108 194 194 231 161 161 161 161 161 161 161 119 119 154 154 154 74 186 150 104 150 104 150 104 83 83 83 102 102 102 102 102 89 89 85 85 87 87 74 77 81 91 91 91 91 91 91 91 91 168 168 168 168 134 134 134 134 134 134 65 65 65 65 65 197 197 90 122 122 94 94 94 94 94 256 103 74 103 74 103 74 95 95 95 95 95 2337 2824 2844 3086 2395 2395 2710 2765 1488 1874 1909 1876 1876 2128 1967 1989 1989 2535 2811 1713 1819 1837 1940 1956 2010 2024 2236 2289 2304 2372 2465 2293 4066 1890 1900 1905 1945 1950 2385 2410 2385 2410 2425 2670 3515 3750 3495 3770 3685 1918 1944 2004 2145 2370 2328 2365 2405 2403 2403 1889 2017 1918 1938 2024 1951 2028 1971 2037 2008 2324 2302 3095 3296 3060 3071 3139 3139 3020 3197 3075 3252 3075 3252 3130 1918 2128 1967 1989 2191 2535 2778 2658 2695 2707 2758 2808 2847 2050 2120 2240 2145 2190 2340 2385 2510 2290 2455 2420 2650 1985 2040 2015 2280 2290 3110 2081 2109 2275 2275 2094 2122 2140 2169 2204 2265 2300 2540 2536 2551 2679 2714 2975 2326 2480 2414 2414 2458 2976 3016 3131 2261 2209 2264 2212 2275 2319 2300 2221 2912 3034 2935 3042 3045 3157 2952 3049 3012 3217 3062 109 136 136 131 108 108 164 164 61 90 90 90 90 98 90 90 90 122 156 92 92 79 92 92 92 92 110 110 110 110 110 110 258 91 91 91 91 91 122 122 122 122 122 140 183 183 183 183 234 92 92 92 98 110 122 122 122 110 110 97 103 97 97 97 97 97 97 97 97 120 120 181 181 181 181 181 181 120 152 120 152 120 152 134 90 98 90 90 98 122 151 121 121 121 121 121 121 97 108 108 108 108 108 108 108 108 108 108 108 92 92 92 92 92 92 98 98 110 110 98 98 98 98 98 98 98 146 146 146 146 146 146 122 110 122 122 122 171 171 171 97 109 97 109 109 97 109 109 141 141 141 141 130 130 141 141 173 145 141 102 115 110 140 101 101 121 121 48 70 70 68 68 102 68 68 68 88 145 58 76 60 76 76 76 76 86 86 86 86 101 100 176 68 68 68 68 68 84 84 84 84 84 120 123 123 123 123 155 68 68 68 102 116 88 88 88 116 116 69 55 69 69 69 69 69 69 69 69 97 97 152 152 152 160 200 160 97 95 95 95 97 95 142 68 102 68 68 68 88 143 110 110 110 110 160 160 69 73 73 82 82 94 82 111 82 94 82 111 62 62 62 62 62 62 70 70 56 56 70 70 70 70 70 112 112 116 116 116 116 116 116 92 73 92 92 92 161 161 156 52 85 52 85 85 68 100 90 114 114 114 114 162 162 114 160 134 106 114 5500 5500 5500 5500 5800 5800 4250 4250 5100 5400 5400 5500 5500 5500 5500 5500 5500 5000 5000 4800 6000 5500 6000 6000 6000 6000 5800 5800 5800 5800 5800 5500 4750 5000 5000 5000 5000 5000 4800 4800 4800 4800 4800 5000 4350 4350 4350 4350 4750 5500 5500 5500 5500 5500 5000 5000 5000 5500 5500 5200 4800 5200 5200 5200 5200 5200 5200 5200 5200 5200 5200 5200 5200 5200 5200 5200 5200 5000 4150 5000 4150 5000 4150 5600 5500 5500 5500 5500 5500 5000 5500 5250 5250 5250 5250 5500 5500 4900 4400 4400 4800 4400 5200 4800 4800 4800 5200 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4500 4500 4800 4800 4800 4800 4800 6600 6600 4800 4800 4800 4800 4800 4800 4200 4500 4200 4200 4200 5200 5200 5200 4800 5250 4800 5250 5250 4500 5500 5500 5400 5400 5400 5400 5100 5100 5400 5300 5500 4800 5400 24 18 19 17 23 23 21 21 47 38 38 37 31 24 31 31 31 24 19 49 31 38 30 30 30 30 27 27 27 27 24 25 15 30 31 31 31 31 26 26 26 26 26 19 22 22 22 22 16 37 31 31 24 23 25 25 25 23 23 31 45 31 31 31 31 31 31 31 31 27 27 17 17 19 19 17 19 19 28 19 28 19 28 18 37 24 31 31 31 24 19 21 21 21 21 19 19 31 26 26 32 28 26 24 24 28 25 23 23 35 31 31 31 27 27 30 30 34 38 38 28 28 29 29 26 26 24 24 24 24 24 24 29 30 27 27 27 20 19 20 37 27 37 27 27 37 26 24 23 23 24 24 17 17 23 19 18 26 19 30 22 25 20 29 29 28 28 53 43 43 41 38 30 38 38 38 30 24 54 38 42 34 34 34 34 33 33 33 33 28 31 19 31 38 38 38 38 32 32 32 32 32 27 25 25 25 25 18 41 38 38 30 30 32 32 32 30 30 37 50 37 37 37 37 37 37 37 37 34 34 22 22 25 25 23 25 24 33 24 33 24 33 24 41 30 38 38 38 30 27 28 28 28 28 26 26 36 31 31 37 33 32 25 29 32 31 29 23 39 38 38 37 32 32 37 37 36 47 47 34 34 34 34 29 29 30 30 30 30 30 30 34 33 32 32 32 24 24 24 46 34 46 34 34 42 32 29 28 28 28 28 22 22 28 25 23 27 25 13950 17450 17710 23875 16430 16925 20970 21105 5151 6295 6575 5572 6377 7957 6229 6692 7609 8921 12964 6479 6855 5399 6529 7129 7295 7295 7895 9095 8845 10295 12945 10345 32250 5195 6095 6795 6695 7395 8845 8495 10595 10245 11245 18280 25552 28248 28176 31600 35056 5389 6189 6669 7689 9959 8499 6989 8189 9279 9279 5499 7099 6649 6849 7349 7299 7799 7499 7999 8249 8949 9549 13499 14399 13499 17199 19699 18399 11900 13200 15580 16900 16630 17950 18150 5572 7957 6229 6692 7609 8921 22018 11850 12170 15040 15510 18150 18620 5118 7053 7603 7126 7775 9960 9233 11259 7463 10198 8013 11694 5348 6338 6488 6918 7898 8778 6938 7198 7898 7788 7738 8358 9258 8058 8238 9298 9538 8449 9639 9989 11199 11549 17669 8948 10698 9988 10898 11248 16558 15998 15690 7775 7975 7995 8195 8495 9495 9995 9980 12940 13415 15985 16515 18420 18950 16845 19045 21485 22470 22625 "symboling" "normalized-losses" "wheel-base" "length" "width" "height" "curb-weight" "engine-size" "bore" "stroke" "compression-ratio" "horsepower" "peak-rpm" "city-mpg" "highway-mpg" "class" "int0" "double1" "int2" "double3" "int4" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"