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Vehicle Specifications and MPG
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About
This collection of technical specifications details various automobile attributes, specifically compiled for machine learning regression tasks focused on predicting city-cycle fuel consumption. The primary goal is to forecast miles per gallon (MPG) based on eight other car characteristics, including both continuous and discrete attributes. The underlying data represents technical specifications of cars downloaded originally from the UCI Machine Learning Repository.
Columns
The data set contains nine attributes:
- mpg: Miles Per Gallon, serving as the label or target attribute for prediction. Values range from 9.0 up to 46.6.
- cylinders: The number of engine cylinders, typically ranging from 3 to 8.
- displacement: Engine displacement, measured in KM.
- horsepower: Engine power, measured in kw.
- weight: Vehicle mass, measured in kg, ranging from 1613 kg up to 5140 kg.
- acceleration: Acceleration rate, measured in m/s2.
- model year: The year of vehicle production, ranging from '70 to '82.
- origin: The geographical origin of the vehicle, represented by discrete values 1, 2, or 3.
- car name: The name of the car model; there are 305 unique names recorded.
Distribution
The data consists of 398 instances (rows) and nine attributes. It is typically available in a CSV file format (
auto-mpg.csv). All attributes display 100% validity within the 398 records, with no identified missing values, mismatched entries, or null data points.Usage
This material is ideal for developing and testing regression models. Key applications include predicting the MPG consumption based on a vehicle's mechanical features, studying the influence of various features on fuel efficiency, and evaluating model performance using statistical measures like R2 and RMSE. It is particularly useful for projects requiring foundational understanding of the data set structure and subsequent data cleanup efforts.
Coverage
The data spans vehicle production model years from 1970 through 1982. The data covers city-cycle fuel consumption metrics. Geographic coverage is indicated by the 'origin' attribute, which uses three discrete values (1, 2, 3) to represent different manufacturing locations.
License
CC0: Public Domain
Who Can Use It
Intended users include machine learning students and practitioners focused on solving regression problems, beginners seeking clean data for initial projects, and researchers analysing historical trends in automobile engineering and fuel economy.
Dataset Name Suggestions
- Auto Fuel Efficiency Predictor
- Vehicle Specifications and MPG
- Automobile Regression Data
- Fuel Consumption Prediction Data
- Car Technical Specs for ML
Attributes
Original Data Source: Vehicle Specifications and MPG
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