This project tries to solve the problem of MPG prediction using the
ANFIS
[Jang93]
approach.
MPG (miles per gallon) is a metrics for gas-efficiency
of automobiles. The problem of MPG prediction is concerned with the
use of attributes of a specific automobile, such as its weight, year,
cylinder number, horse power, and so on, to estimate the MPG of the
automobile. The MPG prediction problem was first study by ...
Description:
The data concerns city-cycle fuel consumption in miles per gallon
(MPG), to be predicted in terms of 3 multivalued discrete and 5
continuous inputs. There are totally 398 instances, each with 8 inputs and
1 output:
Input 1: cylinders (multi-valued discrete)
Input 2: displacement (continuous)
Input 3: horsepower (continuous)
Input 4: weight (continuous)
Input 5: acceleration (continuous)
Input 6: model year (multi-valued discrete)
Input 7: origin (multi-valued discrete)
Input 8: car name (string, unique for each instance)
Output: MPG (continuous)
There are 6 missing values in horsepower.
Past Usage:
The dataset was used in the 1983 American Statistical
Association Exposition.