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Datasets:
scikit-learn
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auto-mpg

Tasks:
Tabular Classification
Tabular Regression
Languages: English
Tags: scikit-learn
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mpg
float64
9
46.6
cylinders
int64
3
8
displacement
float64
68
455
horsepower
stringlengths
1
3
weight
int64
1.61k
5.14k
acceleration
float64
8
24.8
model year
int64
70
82
origin
int64
1
3
car name
stringlengths
6
36
18
8
307
130
3,504
12
70
1
chevrolet chevelle malibu
15
8
350
165
3,693
11.5
70
1
buick skylark 320
18
8
318
150
3,436
11
70
1
plymouth satellite
16
8
304
150
3,433
12
70
1
amc rebel sst
17
8
302
140
3,449
10.5
70
1
ford torino
15
8
429
198
4,341
10
70
1
ford galaxie 500
14
8
454
220
4,354
9
70
1
chevrolet impala
14
8
440
215
4,312
8.5
70
1
plymouth fury iii
14
8
455
225
4,425
10
70
1
pontiac catalina
15
8
390
190
3,850
8.5
70
1
amc ambassador dpl
15
8
383
170
3,563
10
70
1
dodge challenger se
14
8
340
160
3,609
8
70
1
plymouth 'cuda 340
15
8
400
150
3,761
9.5
70
1
chevrolet monte carlo
14
8
455
225
3,086
10
70
1
buick estate wagon (sw)
24
4
113
95
2,372
15
70
3
toyota corona mark ii
22
6
198
95
2,833
15.5
70
1
plymouth duster
18
6
199
97
2,774
15.5
70
1
amc hornet
21
6
200
85
2,587
16
70
1
ford maverick
27
4
97
88
2,130
14.5
70
3
datsun pl510
26
4
97
46
1,835
20.5
70
2
volkswagen 1131 deluxe sedan
25
4
110
87
2,672
17.5
70
2
peugeot 504
24
4
107
90
2,430
14.5
70
2
audi 100 ls
25
4
104
95
2,375
17.5
70
2
saab 99e
26
4
121
113
2,234
12.5
70
2
bmw 2002
21
6
199
90
2,648
15
70
1
amc gremlin
10
8
360
215
4,615
14
70
1
ford f250
10
8
307
200
4,376
15
70
1
chevy c20
11
8
318
210
4,382
13.5
70
1
dodge d200
9
8
304
193
4,732
18.5
70
1
hi 1200d
27
4
97
88
2,130
14.5
71
3
datsun pl510
28
4
140
90
2,264
15.5
71
1
chevrolet vega 2300
25
4
113
95
2,228
14
71
3
toyota corona
25
4
98
?
2,046
19
71
1
ford pinto
19
6
232
100
2,634
13
71
1
amc gremlin
16
6
225
105
3,439
15.5
71
1
plymouth satellite custom
17
6
250
100
3,329
15.5
71
1
chevrolet chevelle malibu
19
6
250
88
3,302
15.5
71
1
ford torino 500
18
6
232
100
3,288
15.5
71
1
amc matador
14
8
350
165
4,209
12
71
1
chevrolet impala
14
8
400
175
4,464
11.5
71
1
pontiac catalina brougham
14
8
351
153
4,154
13.5
71
1
ford galaxie 500
14
8
318
150
4,096
13
71
1
plymouth fury iii
12
8
383
180
4,955
11.5
71
1
dodge monaco (sw)
13
8
400
170
4,746
12
71
1
ford country squire (sw)
13
8
400
175
5,140
12
71
1
pontiac safari (sw)
18
6
258
110
2,962
13.5
71
1
amc hornet sportabout (sw)
22
4
140
72
2,408
19
71
1
chevrolet vega (sw)
19
6
250
100
3,282
15
71
1
pontiac firebird
18
6
250
88
3,139
14.5
71
1
ford mustang
23
4
122
86
2,220
14
71
1
mercury capri 2000
28
4
116
90
2,123
14
71
2
opel 1900
30
4
79
70
2,074
19.5
71
2
peugeot 304
30
4
88
76
2,065
14.5
71
2
fiat 124b
31
4
71
65
1,773
19
71
3
toyota corolla 1200
35
4
72
69
1,613
18
71
3
datsun 1200
27
4
97
60
1,834
19
71
2
volkswagen model 111
26
4
91
70
1,955
20.5
71
1
plymouth cricket
24
4
113
95
2,278
15.5
72
3
toyota corona hardtop
25
4
97.5
80
2,126
17
72
1
dodge colt hardtop
23
4
97
54
2,254
23.5
72
2
volkswagen type 3
20
4
140
90
2,408
19.5
72
1
chevrolet vega
21
4
122
86
2,226
16.5
72
1
ford pinto runabout
13
8
350
165
4,274
12
72
1
chevrolet impala
14
8
400
175
4,385
12
72
1
pontiac catalina
15
8
318
150
4,135
13.5
72
1
plymouth fury iii
14
8
351
153
4,129
13
72
1
ford galaxie 500
17
8
304
150
3,672
11.5
72
1
amc ambassador sst
11
8
429
208
4,633
11
72
1
mercury marquis
13
8
350
155
4,502
13.5
72
1
buick lesabre custom
12
8
350
160
4,456
13.5
72
1
oldsmobile delta 88 royale
13
8
400
190
4,422
12.5
72
1
chrysler newport royal
19
3
70
97
2,330
13.5
72
3
mazda rx2 coupe
15
8
304
150
3,892
12.5
72
1
amc matador (sw)
13
8
307
130
4,098
14
72
1
chevrolet chevelle concours (sw)
13
8
302
140
4,294
16
72
1
ford gran torino (sw)
14
8
318
150
4,077
14
72
1
plymouth satellite custom (sw)
18
4
121
112
2,933
14.5
72
2
volvo 145e (sw)
22
4
121
76
2,511
18
72
2
volkswagen 411 (sw)
21
4
120
87
2,979
19.5
72
2
peugeot 504 (sw)
26
4
96
69
2,189
18
72
2
renault 12 (sw)
22
4
122
86
2,395
16
72
1
ford pinto (sw)
28
4
97
92
2,288
17
72
3
datsun 510 (sw)
23
4
120
97
2,506
14.5
72
3
toyouta corona mark ii (sw)
28
4
98
80
2,164
15
72
1
dodge colt (sw)
27
4
97
88
2,100
16.5
72
3
toyota corolla 1600 (sw)
13
8
350
175
4,100
13
73
1
buick century 350
14
8
304
150
3,672
11.5
73
1
amc matador
13
8
350
145
3,988
13
73
1
chevrolet malibu
14
8
302
137
4,042
14.5
73
1
ford gran torino
15
8
318
150
3,777
12.5
73
1
dodge coronet custom
12
8
429
198
4,952
11.5
73
1
mercury marquis brougham
13
8
400
150
4,464
12
73
1
chevrolet caprice classic
13
8
351
158
4,363
13
73
1
ford ltd
14
8
318
150
4,237
14.5
73
1
plymouth fury gran sedan
13
8
440
215
4,735
11
73
1
chrysler new yorker brougham
12
8
455
225
4,951
11
73
1
buick electra 225 custom
13
8
360
175
3,821
11
73
1
amc ambassador brougham
18
6
225
105
3,121
16.5
73
1
plymouth valiant
16
6
250
100
3,278
18
73
1
chevrolet nova custom
18
6
232
100
2,945
16
73
1
amc hornet
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Auto Miles per Gallon (MPG) Dataset

Following description was taken from UCI machine learning repository. Source: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.

Data Set Information:

This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute. The original dataset is available in the file "auto-mpg.data-original".

"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)

Attribute Information:

  • mpg: continuous
  • cylinders: multi-valued discrete
  • displacement: continuous
  • horsepower: continuous
  • weight: continuous
  • acceleration: continuous
  • model year: multi-valued discrete
  • origin: multi-valued discrete
  • car name: string (unique for each instance)
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