Auto Miles per Gallon (MPG) Dataset

Following description was taken from UCI machine learning repository. Source: 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.

  Data Set 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… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/auto-mpg.

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.

Analysis of ‘Auto MPG Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasserh/auto-mpg-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

https://raw.githubusercontent.com/Masterx-AI/Project_Auto_MPG_Prediction_/main/a.jpg" alt="">

Description:

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

"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

Acknowledgements

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

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Wei Qian

Contents

Dataset

This dataset was created by Sara Bouzarwal

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 sjhdgdbdgh

Released under CC0: Public Domain

Contents

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

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.

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

This statistic represents unadjusted fuel economy levels for MY 2016 light-duty vehicles sold in the United States, by manufacturer and vehicle type. MY 2016 Toyota passenger cars on U.S. roads had an average fuel economy standard of some 40 miles per gallon.

The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes.

Number of Instances: 398

Number of Attributes: 9 including the class attribute

Attributes

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)

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: May 20, 2025)

Model year 2024 pickups on U.S. roads had a preliminary average 21 miles per gallon fuel economy. Adjusted fuel economy values reflect real-world estimates. Since 2022, car sport-utility vehicles have surpassed sedans and wagons as the most fuel efficient type of vehicle.

A dataset of official CO2 emissions (g/km), fuel economy (MPG), and BIK prices for cars by type, make, and model.

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.

NHTSA's Corporate Average Fuel Economy (CAFE) program requires manufacturers of passenger cars and light trucks, produced for sale in the U.S., to meet CAFE standards, expressed in miles per gallon (mpg). The purpose of the CAFE program is to reduce the nation's energy consumption by increasing the fuel economy of cars and light trucks. The CAFE Public Information Center (PIC) is the authoritative source for Corporate Average Fuel Economy (CAFE) program data. This site allows fuel economy data to be viewed in report and/or graph format. The data can be sorted and filtered to produce custom reports which can also be downloaded as Excel or pdf files. NHTSA periodically updates the CAFE data in the PIC and, therefore, each report and graph is date stamped to indicate the last time NHTSA made updates.

The Fuel Economy Label and CAFE Data asset contains measured summary fuel economy estimates and test data for light-duty vehicle manufacturers by model for certification as required under the Energy Policy and Conservation Act of 1975 (EPCA) and The Energy Independent Security Act of 2007 (EISA) to collect vehicle fuel economy estimates for the creation of Economy Labels and for the calculation of Corporate Average Fuel Economy (CAFE). Manufacturers submit data on an annual basis, or as needed to document vehicle model changes.The EPA performs targeted fuel economy confirmatory tests on approximately 15% of vehicles submitted for validation. Confirmatory data on vehicles is associated with its corresponding submission data to verify the accuracy of manufacturer submissions beyond standard business rules. Submitted data comes in XML format or as documents, with the majority of submissions being sent in XML, and includes descriptive information on the vehicle itself, fuel economy information, and the manufacturer's testing approach. This data may contain proprietary information (CBI) such as information on estimated sales or other data elements indicated by the submitter as confidential. CBI data is not publically available; however, within the EPA data can accessed under the restrictions of the Office of Transportation and Air Quality (OTAQ) CBI policy [RCS Link]. Datasets are segmented by vehicle model/manufacturer and/or year with corresponding fuel economy, test, and certification data. Data assets are stored in EPA's Verify system.Coverage began in 1974 with early records being primarily paper documents which did not go through the same level of validation as primarily digital submissions which started in 2008. Early data is available to the public digitally starting from 1978, but more complete digital certification data is available starting in 2008. Fuel economy submission data prior to 2006 was calculated using an older formula; however, mechanisms exist to make this data comparable to current results.Fuel Economy Label and CAFE Data submission documents with metadata, certificate and summary decision information is utilized and made publically available through the EPA/DOE's Fuel Economy Guide Website (https://www.fueleconomy.gov/) as well as EPA's Smartway Program Website (https://www.epa.gov/smartway/) and Green Vehicle Guide Website (http://ofmpub.epa.gov/greenvehicles/Index.do;jsessionid=3F4QPhhYDYJxv1L3YLYxqh6J2CwL0GkxSSJTl2xgMTYPBKYS00vw!788633877) after it has been quality assured. Where summary data appears inaccurate, OTAQ returns the entries for review to their originator.

Model year (MY) 2023 Stellantis cars and light trucks on U.S. roads had an average fuel economy standard of some 21.8 miles per gallon, the lowest value of all manufacturers. The average MY 2023 vehicle sold in the U.S. needs almost 3.7 gallons to travel 100 miles. Adjusted fuel economy values reflect real world estimates.