Auto MPG
Donated on 7/6/1993
Revised from CMU StatLib library, data concerns city-cycle fuel consumption
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Regression
Feature Type
Real, Categorical, Integer
# Instances
398
# Features
7
Dataset Information
Additional 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)
Has Missing Values?
Yes
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| displacement | Feature | Continuous | no | |||
| mpg | Target | Continuous | no | |||
| cylinders | Feature | Integer | no | |||
| horsepower | Feature | Continuous | yes | |||
| weight | Feature | Continuous | no | |||
| acceleration | Feature | Continuous | no | |||
| model_year | Feature | Integer | no | |||
| origin | Feature | Integer | no | |||
| car_name | ID | Categorical | no |
0 to 9 of 9
Additional Variable Information
1. mpg: continuous 2. cylinders: multi-valued discrete 3. displacement: continuous 4. horsepower: continuous 5. weight: continuous 6. acceleration: continuous 7. model year: multi-valued discrete 8. origin: multi-valued discrete 9. car name: string (unique for each instance)
Baseline Model Performance
Papers Citing this Dataset
By Marvin Wright, Inke König. 2019
Published in PeerJ.
By Yuntian Chen, Haibin Chang, Meng Jin, Dongxiao Zhang. 2019
Published in Neural Networks, 110, 170-185 (2019).
By Varun Ojha, Vaclav Snasel, Ajith Abraham. 2017
Published in IEEE Transactions on Fuzzy Systems 2017.
By Lingzhu Li, Lixing Zhu. 2017
Published in
0 to 5 of 15
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset auto_mpg = fetch_ucirepo(id=9) # data (as pandas dataframes) X = auto_mpg.data.features y = auto_mpg.data.targets # metadata print(auto_mpg.metadata) # variable information print(auto_mpg.variables)
Quinlan,R.. (1993). Auto MPG. UCI Machine Learning Repository. https://doi.org/10.24432/C5859H.
@misc{misc_auto_mpg_9,
author = {Quinlan,R.},
title = {{Auto MPG}},
year = {1993},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5859H}
}Keywords
Creators
R. Quinlan
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.