Context

The data is technical spec of cars. The dataset is downloaded from UCI Machine Learning Repository

Content

1. Title: Auto-Mpg Data

2. 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

3. 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.

4. 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)

5. Number of Instances: 398

6. Number of Attributes: 9 including the class attribute

7. Attribute 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)

8. Missing Attribute Values: horsepower has 6 missing values

Acknowledgements

Dataset: UCI Machine Learning Repository Data link : https://archive.ics.uci.edu/ml/datasets/auto+mpg

Inspiration

I have used this dataset for practicing my exploratory analysis skills.

Dataset

This dataset was created by Eda Cebeci

Contents

This dataset was taken by UCL resources.

Here's the explanation of the dataset.

mpg: Miles per gallon (fuel efficiency) cylinders: Number of cylinders in the engine displacement: Engine displacement in cubic inches horsepower: Horsepower of the engine weight: Weight of the car in pounds acceleration: Acceleration rate (0-60 mph time) in seconds model year: Year the car was manufactured (e.g., 70 for 1970) origin: Origin of the car (1: American, 2: European, 3: Japanese) car name: Name of the car

We need to predict mpg by doing couple of feature engineering and machine learning applications.

Dataset Card for "auto-mpg"

More Information needed

The dataset used in the paper is a real-world dataset, specifically the Auto-MPG dataset from the University of California, Irvine (UCI) database.

This dataset has been curated to facilitate machine learning tasks aimed at predicting fuel efficiency of vehicles. The original data has been meticulously cleaned and reduced to include only the most relevant features, enhancing usability for modeling and analysis.

Features: r: Range, possibly indicating the total driving range of the vehicle. m (kg): Mass of the vehicle in kilograms. Mt: CO2 emissions in metric tons. Ewltp (g/km): CO2 emissions measured under the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) in grams per kilometer. Ft: Type of fuel used by the vehicle (e.g., petrol, diesel, electric). Fm: Composition of the fuel mix used by the vehicle. ec (cm3): Engine capacity in cubic centimeters. ep (KW): Engine power output in kilowatts. z (Wh/km): Energy consumption in watt-hours per kilometer. Erwltp (g/km): CO2 emissions reduction in grams per kilometer measured under WLTP. Fuel consumption: The target variable representing the fuel consumption of the vehicle. Electric range (km): The maximum distance the vehicle can travel on electric power alone.

Usage: This dataset is ideal for machine learning practitioners and researchers interested in developing models to predict fuel efficiency based on various vehicle characteristics. It is well-suited for regression tasks, exploratory data analysis, and feature engineering exercises.

Acknowledgements: This dataset has been prepared and preprocessed to ensure high quality and relevance. Special thanks to the original data providers and contributors who made this data available for public use.

Dataset Card for "auto-mpg-split"

More Information needed

Dataset

This dataset was created by royalstag

Contents

From https://archive.ics.uci.edu/ml/datasets/Auto+MPG Preprocessing attempted to replicate Altendorf et al. 2012 Learning from Sparse Data by Exploiting Monotonicity Constraints: - MPG was binarised into -1 (<=28MPG) and +1 (>28 MPG). - Origin was also binarised into 0 (US/EUR), and 1 (Japan) - Missing Values: 6 rows from original removed due to missing 'hp' values.

Dataset

This dataset was created by Wei Qian

Contents

Vehicle fuel economy and other emissions statistics, fuel/engine/drivetrain types, and other vehicle and costing information.

Dataset

This dataset was created by Nandini_18

Contents

This dataset contains the average monthly fuel efficiency (miles per gallon) for Cary's hybrid vehicles.

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. This is my own variation with consecutive ID added.

Model year (MY) 2023 Stellantis cars and light trucks on U.S. roads had an average fuel economy standard of some **** miles per gallon, the lowest value of all manufacturers. The average MY 2023 vehicle sold in the U.S. needs almost *** gallons to travel 100 miles. Adjusted fuel economy values reflect real world estimates.

Context

I am uploading this particular data set as I was working on this since a week, and I found it very useful for beginners. I have tried to built linear regression model on this dateset, as I learnt R programming recently.

Content

Attribute Information:

mpg: continuous variable (dependent variable) displacement: continuous variable horsepower: continuous variable cylinders: multi-valued discrete weight: continuous variable acceleration: continuous variable model year: multi-valued discrete origin: multi-valued discrete car name: string

Note that horsepower has 6 missing values Also note that In this dataset mpg is our dependent variable.

Source

Sources: (a) Origin: Dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. American Statistical Association Exposition used this dataset in year 1983. (c) Date: July 7, 1993

Data sets provide model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. To help you compare vehicles from different model years, the fuel consumption ratings for 1995 to 2014 vehicles have been adjusted to reflect 5-cycle testing. Note that these are approximate values that were generated from the original ratings, not from vehicle testing. For more information on fuel consumption testing, visit: https://natural-resources.canada.ca/energy-efficiency/transportation-energy-efficiency/fuel-consumption-testing. To compare the fuel consumption information of new and older models to find the most fuel-efficient vehicle that meets your everyday needs, use the fuel consumption ratings search tool at https://fcr-ccc.nrcan-rncan.gc.ca/en. (Data update: July 24, 2025)

Dataset showing how much fuel each council vehicle has consumed, by year and by fuel type.

Dataset guidance

The following information refers to the columns in the data:

• Fleet = fleet number (specific number used to identify a vehicle)
• Vehicle = vehicle type
• Fuel = fuel used in litres
• Dist.Run = Total mileage for the reporting period
• MPG = Miles Per Gallon
• Type = D: Diesel, G: Gas Oil

Due to problems and changes with the fuel system software this data is not available for 2017/18. Data for 2018/19 will be published.

Corporate average fuel economy (CAFE) standards in the United States indicate that new passenger cars will average **** miles per gallon in 2025, meaning that drivers of typical passenger vehicles in the U.S. will have to stop more often to refuel than Chinese and European motorists. By 2025, new passenger vehicles in Europe are expected to average **** miles per gallon, while new cars on Chinese roads are anticipated to travel **** miles per gallon. For every 100 miles driven, that is just over *** gallons in China.

A dataset of vehicle MPG ratings and fuel cost calculations based on manufacturer, model, and fuel type.