This dataset captures detailed specifications of 10,000 cars manufactured between 2010 and 2020. It provides a comprehensive resource for various analytical and machine learning projects, including price prediction, market trend analysis, and comparative studies among different car makes and models.

The dataset includes essential attributes such as the make and model of the car, the year of manufacture, engine size, fuel type, and the price in USD. These attributes allow for in-depth analysis and modeling, facilitating insights into market trends, price prediction, and comparative studies among different car makes and models.

The car makes and models were selected from popular vehicles available in the market, reflecting consumer preferences and market availability. The prices, engine sizes, and fuel types were randomly assigned within realistic ranges, ensuring that the dataset remains representative and suitable for various analytical purposes.

Whether you are an automotive enthusiast, a data scientist, or a machine learning practitioner, this dataset offers a rich foundation for your projects. It can be used to develop machine learning models to predict car prices, analyze trends in the automotive market, compare different car makes and models, study the distribution and evolution of fuel types, or for educational purposes in data science and machine learning courses.

The data was meticulously generated using the Faker library to simulate realistic car specifications. This dataset is versatile and can be used for a wide array of projects, making it a valuable tool in your analytical and predictive endeavors.

Car Production in the United States increased to 11.04 Million Units in August from 10.42 Million Units in July of 2025. This dataset provides - United States Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

Dataset Description:

The dataset appears to contain information related to car sales, with various attributes providing details about different car models. Below is a description of the attributes in the dataset:

Attribute Descriptions:

1. Manufacturer: The name of the car manufacturer (e.g., Acura, Audi, BMW).
2. Model: The specific model of the car (e.g., Integra, TL, A4).
3. Sales_in_thousands: The number of car sales in thousands.
4. _year_resale_value: The estimated resale value of the vehicle after a certain number of years.
5. Vehicle_type: The type of vehicle (e.g., Passenger, Car).
6. Price_in_thousands: The price of the car in thousands of dollars.
7. Engine_size: The size of the car's engine.
8. Horsepower: The horsepower of the car's engine.
9. Wheelbase: The wheelbase of the car (distance between the front and rear axles).
10. Width: The width of the car.
11. Length: The length of the car.
12. Curb_weight: The weight of the car without occupants or baggage.
13. Fuel_capacity: The capacity of the fuel tank.
14. Fuel_efficiency: The fuel efficiency of the car.
15. Latest_Launch: The date of the latest product launch.
16. Power_perf_factor: A performance factor associated with power.

Total Vehicle Sales in the United States decreased to 16.10 Million in August from 16.40 Million in July of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The dataset could include various features and measurements related to the engine health of vehicles, such as engine RPM, temperature, pressure, and other sensor data. It may also include metadata on the vehicle, such as make, model, year, and mileage.

One potential project using this dataset could be to build a predictive maintenance model for automotive engines. By analyzing the patterns and trends in the data, machine learning algorithms could be trained to predict when an engine is likely to require maintenance or repair. This could help vehicle owners and mechanics proactively address potential issues before they become more severe, leading to better vehicle performance and longer engine lifetimes.

Another potential use for this dataset could be to analyze the performance of different types of engines and vehicles. Researchers could use the data to compare the performance of engines from different manufacturers, for example, or to evaluate the effectiveness of different maintenance strategies. This could help drive innovation and improvements in the automotive industry.

The number of new and used vehicles and the sales dollars respectively sold by month. MDOT MVA’s Customer Connect modernization project, implemented in July 2020, has increased the amount of data that is collected and used to calculate car sales. This data is updated in real time and may fluctuate based on external factors, including electronic submissions from dealers and other vendors.

mammoth666/cars-make-model-year-chunk-115 dataset hosted on Hugging Face and contributed by the HF Datasets community

Analysis of ‘Car Prices Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sidharth178/car-prices-dataset on 29 August 2021.

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

Context

With the rise in the variety of cars with differentiated capabilities and features such as model, production year, category, brand, fuel type, engine volume, mileage, cylinders, colour, airbags and many more, we are bringing a car price prediction challenge for all. We all aspire to own a car within budget with the best features available. To solve the price problem we have created a dataset of 19237 for the training dataset and 8245 for the test dataset.

Data Description

Train.csv - 19237 rows x 18 columns (Includes Price Columns as Target) - Attributes - ID - Price: price of the care(Target Column) - Levy - Manufacturer - Model - Prod. year - Category - Leather interior - Fuel type - Engine volume - Mileage - Cylinders - Gear box type - Drive wheels - Doors - Wheel - Color - Airbags Test.csv - 8245 rows x 17 columns

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

Average car price in Brazil

Abou the data

Data extracted from FIPE.

Every observation (row) corresponds to a average car price, calculated to a month of a year of reference. In other words, in a year (year_of_reference) there are 12 observations related to the same car, however avg_price_brl might differ.

The variables fuel, gear and engine_size were extracted from values of column model, as in original there is no column dedicated to those values. Since some values for model don't contain the information of engine size, this dataset doesn't contain the whole data from FIPE's original. Additionally, if 'Aut.' is not present in model, the car is assumed to be manual.

The prices are calculated by FIPE and they're here as original (in BRL).

FIPE updates the information on a monthly basis. Here, the referred month is given as month_of_reference, as the corresponding year has variable named as year_of_reference

The FIPE codes (fipe_codes) are the model identifier used in FIPE webpage.

Variables

• year_of_reference: year of reference of the observation, i.e., the year the data corresponds to.
• moth_of_reference: month of reference of the observation, i.e., the month the data corresponds to. The average price is calculated by FIPE each month.
• fipe_code: unique id corresponding to a model for easy search on FIPE webpage.
• authentication: unique code that authenticates the consult in FIPE's site.
• brand: car's make.
• model: a description of the car containing the name and other descriptive information, as provided in FIPE table.
• fuel: fuel used by the car. Some of gas cars are actually alcohol and gas (totalflex), which is common in Brazil.
• gear: the way gears are shifted.
• engine_size: Engine size measured in cubic centimeters.
• year_model: those values corresponds to the year of reference, and may not be the same of the year of manufacture, which in case will corresponds to a year before year_model. Observations with year_model = year_of_reference mean the car is brand new for that year of reference, i.e., a 2021 car with year_of_reference = 2021 and moth_of_reference = July mean that the observation (mainly the average price) corresponds to a brand new car in the year of 2021, of the month of July. The same model may have a different average price for different month.
• avg_price_brl: average car's price, as measured by FIPE, in BRL.

- (variable deleted in Version 2).

Questions

Here are some questions that may be used as starting point of analysis.

• How average prices depend on car's age?
• How the engine size contribute to the average price?
• Can FIPE's prices be used to predict car prices?
• Gather some data from real stores. Can you compute you own average? How's it different from FIPE's?
• Build a time series and make explore them accordingly to engine size and gear type.
• Scale prices with Brazil's minimum wage in order make analysis period wise.`

Dataset containing vehicles sold in the US market of 2024-2025 year. Compares horsepower, torque, weight, and ratios of all makes and models sold in the US market of 2024-2025.

Data is taken from manufacturer website and Car & Driver where applicable.

I only compared data with vehicles designed, marketed, and sold as sedans or lower. Wagons were included where applicable. The Mercedes E-class wagon was excluded due to lack of data found. Data excludes vehicles sold and marketed as CUV and above (CUVs, SUVs, Trucks, Vans, etc.)

mammoth666/cars-make-model-year-chunk-126 dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset provides values for CAR PRODUCTION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Context

Toy Cars annotated on YOLO format to be used in any YOLO model for training. .classes, .cfg and .data files are inside the dataset as well. You can use this dataset for the detection of toy cars since they somewhat differ from real-world cars. The detection model can be used in prototypes where you need to place toy cars for projection.

Content

It contains .txt files as well as the image files of around 1000 cars.

Hey everyone, In my final year project, I created Smart Traffic Management System. The project was to manage traffic lights' delays based on the number of vehicles on road. I made everything worked using Raspberry Pi and pre-recorded videos but it was a "final year project", it was needed to be tested by changing videos frequently which was a kind of hustle. Collecting tons of videos and loading them in Pi was not too hard but it would have cost time, by every time changing names of videos in the code. Also, it was not possible to implement it in real (unless govt. would have permitted me, hehe). So I chose to showcase my work by making a beautiful prototype. I know, the image isn't so appealing, I apologise for that, but you got the idea, right. I placed my cars on tracks and took real-time video of the lanes from the two cameras attached to two big sticks.

Why only two cameras when there are four roads? Raspberry Pi supports only two cameras. In my case, the indexes were 0 and 2. But to make things work as I have planned, I cropped images for each lane.

What does it mean? Let us take one camera and the respective two roads as an example. I took real-time video, performed image framing on it. Since the roads beneath the cars were supposed to be still (obvio, cars move, not roads :>), I performed image framing after every 2 seconds of the video. The images were first cropped and then saved in the Pi. I resized the images, found the coordinates on which the two roads were separating, cropped the image till those coordinates and got 2 images of 2 separate roads from 1 camera.

Finally, I ran my code and I found it could only detect a few cars. I thought real and toy ones looked quite similar, but the model didn't think the same. My YOLO weight file was trained on original cars and now I had to do training, again.

I looked for datasets already available but couldn't find any. So I decided to make one. I collected images from different web sources and performed the most important task on each of them. ANNOTATION, using LabelImg. I separately annotated around 1000 images, in YOLO format, did all the processing and created this dataset. Usually, for YOLO especially, you get pictures on the internet but not text files. You have to individually perform annotation on all of them. It takes time and there isn't any tool to do it in bulk because you have to properly tell how many cars are there in the picture. Maybe in the future, LableImg gets updated with some machine learning algorithm for detecting and annotating images automatically (who knows). So here it is for your help. I will be adding the notebook as well in some time. Any questions? drop down below. Do upvote if it’s helpful. You can find me on: https://www.github.com/tubasid https://www.linkedin.com/in/tubasid https://www.twitter.com/in/tubaasid https://www.discord.com/channels/@tubasid#2498 Until next post. TubaSid

In 2024, the auto industry in the United States sold approximately 15.9 million light vehicle units. This figure includes retail sales of about three million passenger cars and just under 12.9 million light trucks. Lower fuel consumption There are many kinds of light vehicles available in the United States. Light-duty vehicles are popular for their utility and improved fuel economy, making them an ideal choice for savvy consumers. As of Model Year 2023, the light vehicle manufacturer with the best overall miles per gallon was Kia, with one gallon of gas allowing for 30.4 miles on the road. Higher brand satisfaction When asked about light vehicle satisfaction, consumers in the United States were most satisfied with Toyota, Subaru, Tesla, and Mercedes-Benz models. Another survey conducted in 2018 and quizzing respondents on their stance regarding the leading car brands indicated that Lexus was among the most dependable brands based on the number of problems reported per 100 vehicles.

mammoth666/cars-make-model-year-chunk-30 dataset hosted on Hugging Face and contributed by the HF Datasets community

mammoth666/cars-make-model-year-chunk-177 dataset hosted on Hugging Face and contributed by the HF Datasets community

These datasets provide vehicle counts broken down by ZIP code, model year, fuel type, make and duty (light/heavy) of registered vehicles with specific as of dates.