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.

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 ---

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.

Dataset

This dataset was created by deepakshi dang

Contents

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

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 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.

Dataset

This dataset was created by Berna Nur Çetinkaya

Contents

Dataset

This dataset was created by Nelson Doss

Contents

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

Dataset

This dataset was created by Wei Qian

Contents

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: June 10, 2025)

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.

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

Dataset

This dataset was created by Sara Bouzarwal

Contents

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.

This is a report of city vehicles and actual MPG compared to EPA estimated MPG. Each line of data is a combination of all the active vehicles on the city’s telematics system broken down into year/make/model/standard type with fueling and usage data. The intent is for each line to represent the sticker MPG and the real-world MPG and how these compare to each other. The report can be found at https://www1.nyc.gov/assets/dcas/downloads/pdf/fleet/NYC-Fleet-Newsletter-306-May-27-2020-Hybrids-Work-Even-Better-in-Reality-Than-in-Theory.pdf.

Dataset

This dataset was created by Armanjit Singh

Contents

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.