A machine learning model builded on python, using multiple linear regression to predict MPG consumption of vehicules.
Created with Mohamed EL-ELIEM "med.eleliem@gmail.com"
https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/
-
Title: Auto-Mpg Data
-
Sources: (a) Origin: 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. (c) Date: July 7, 1993
-
Past Usage:
- See 2b (above)
- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
-
Relevant 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)
-
Number of Instances: 398
-
Number of Attributes: 9 including the class attribute
-
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)
-
Missing Attribute Values: horsepower has 6 missing values