Car Production in the United States increased to 11.19 Million Units in May from 10.44 Million Units in April of 2025. This dataset provides - United States Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2023, some 94 million motor vehicles were produced worldwide. This figure translates into an increase of around 10 percent compared with the previous year. China, Japan, and Germany were among the largest producers of cars and commercial vehicles.

China leads production

China is ranked as the largest passenger car manufacturer in the world, having produced more than 26 million cars in 2023, and accounting for almost one third of the world’s passenger vehicle production. Over the past decades, China has emerged as one of the main growth markets for players in the global automobile industry.

American manufacturers in China

One of China’s largest car manufacturing companies is the joint venture between General Motors and SAIC Motor Corporation Limited, known as Shanghai General Motors Company Ltd or simply Shanghai GM. GM produces and sells passenger vehicles under the Chevrolet and Cadillac brands, among others. Aside from manufacturing cars, the company also produces engines and transmission systems. Shanghai GM’s production amounted to a little over 1.3 million units in 2021.

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.

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.

Total Vehicle Sales in the United States decreased to 15.30 Million in June from 15.70 Million in May 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.

Application and use cases

1 )Market Analysis: Evaluate overall trends and regional variations in car sales to assess manufacturer performance, model preferences, and demographic insights. 2) Seasonal Patterns and Competitor Analysis: Investigate seasonal and cyclical patterns in sales. 3) Forecasting and Predictive Analysis Use historical data for forecasting and predict future market trends. Support marketing, advertising, and investment decisions based on insights. 4) Supply Chain and Inventory Optimization: Provide valuable data for stakeholders in the automotive industry.

Motor vehicle sales grew by some 11.9 percent worldwide between 2022 and 2023. Passenger vehicles increased by around 11.3 percent compared to the previous year when some 58.6 million cars were sold worldwide. The current state of the market In 2023, motor vehicle sales reached over 92.7 million units worldwide. China was the largest automobile market worldwide, making up close to 25.8 million of the new car registrations that same year. The United States and Europe ranked second and third, with light vehicle sales reaching approximately 15.5 million units in the U.S. market. The German-based Volkswagen Group and Japanese Toyota Motor were the global leading automakers, with revenues reaching around 348.6 and 311.9 billion U.S. dollars respectively as of May 2024. The path to recovery The automotive chip shortage led to around 11.3 million vehicles being cut from worldwide production in 2021, and forecasts estimate that these disruptions in the automotive supply chain will contribute to the removal of another seven million units from production in 2022. However, despite these challenges, the demand for passenger cars increased in 2021 and 2022, as car sales slowly started to increase. This is partly due to consumers' interest in electric vehicles. Autonomous,electrified, and battery electric vehicles are also forecast to gain popularity in the next decades. Electrified vehicles are projected to make up close to a quarter of car sales worldwide by 2025. By 2040, China is forecast to be one of the largest market for autonomous vehicle sales.

Car Production in Germany decreased to 359700 Units in June from 366879 Units in May of 2025. This dataset provides - Germany Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset Overview

The "Vehicle Dataset 2024" provides a comprehensive look at new vehicles available in the market, including SUVs, cars, trucks, and vans. This dataset contains detailed information on various attributes such as make, model, year, price, mileage, and more. With 1002 entries and 18 columns, this dataset is ideal for data science enthusiasts and professionals looking to practice data cleaning, exploratory data analysis (EDA), and predictive modeling.

Data Science Applications

Given the richness of the data, this dataset can be used for a variety of data science applications, including but not limited to: - Price Prediction: Build models to predict vehicle prices based on features such as make, model, year, and mileage. - Market Analysis: Perform market segmentation and identify trends in vehicle types, brands, and pricing. - Descriptive Statistics: Conduct comprehensive descriptive statistical analyses to summarize and describe the main features of the dataset. - Visualization: Create visualizations to illustrate the distribution of prices, mileage, and other features across different vehicle types. - Data Cleaning: Practice data cleaning techniques, handling missing values, and transforming data for further analysis. - Feature Engineering: Develop new features to improve model performance, such as price per year or mileage per year.

Column Descriptors

1. name: The full name of the vehicle, including make, model, and trim.
2. description: A brief description of the vehicle, often including key features and selling points.
3. make: The manufacturer of the vehicle (e.g., Ford, Toyota, BMW).
4. model: The model name of the vehicle.
5. type: The type of the vehicle, which is "New" for all entries in this dataset.
6. year: The year the vehicle was manufactured.
7. price: The price of the vehicle in USD.
8. engine: Details about the engine, including type and specifications.
9. cylinders: The number of cylinders in the vehicle's engine.
10. fuel: The type of fuel used by the vehicle (e.g., Gasoline, Diesel, Electric).
11. mileage: The mileage of the vehicle, typically in miles.
12. transmission: The type of transmission (e.g., Automatic, Manual).
13. trim: The trim level of the vehicle, indicating different feature sets or packages.
14. body: The body style of the vehicle (e.g., SUV, Sedan, Pickup Truck).
15. doors: The number of doors on the vehicle.
16. exterior_color: The exterior color of the vehicle.
17. interior_color: The interior color of the vehicle.
18. drivetrain: The drivetrain of the vehicle (e.g., All-wheel Drive, Front-wheel Drive).

Ethically Mined Data

This dataset was ethically mined from cars.com using an API provided by Apify. All data collection practices adhered to the terms of service and privacy policies of the source website, ensuring the ethical use of data.

Acknowledgements

• Apify: For providing the API used to scrape the data from cars.com.
• Cars.com: For being the source of the vehicle data.
• DALL-E 3: For generating the thumbnail image for this dataset.

A car (or automobile) is a wheeled motor vehicle that is used for transportation. Most definitions of cars say that they run primarily on roads, seat one to eight people, have four wheels, and mainly transport people instead of goods.[1][2]

The year 1886 is regarded as the birth year of the car, when German inventor Carl Benz patented his Benz Patent-Motorwagen.[3][4][5] Cars became widely available during the 20th century. One of the first cars affordable by the masses was the 1908 Model T, an American car manufactured by the Ford Motor Company. Cars were rapidly adopted in the US, where they replaced animal-drawn carriages and carts.[6] In Europe and other parts of the world, demand for automobiles did not increase until after World War II.[6] The car is considered an essential part of the developed economy.

The source of dataset is ../input/cars-cars-2/Cars_India_dataset.csv The dataset contains the following columns. 0 Model object - Model of the cars 1 Maker object - Maker of cars 2 Type object - type of cars 3 Seats int64 - number of seats in cars 4 Displacement float64 - displacement of car 5 Length int64 - length of car 6 Width int64 - width of car 7 Height int64 - height of car 8 Wheelbase int64 - wheel base of car 9 No_of_Cylinders float64 - number of cylinders in car 10 Fuel object - type of fuel in car 11 Engine Type object - type of engime 12 Transmission object - transmission in car 13 Front Brake object - front break of car 14 Rear Brake object - rear break of car 15 Drive object - drive of car 16 Turning Radius float64 - turning radius of car 17 Fuel Tank Capacity float64 - fuel capacity of car 18 Boot Space float - boot space

Tesla Inc.’s most recent quarterly vehicle production volume came to nearly ******* units. Tesla's production level in the first quarter of 2023 decreased by some **** percent quarter-on-quarter and by approximately **** percent year-on-year. Growth amid crisis It was anticipated that the coronavirus outbreak in China would affect the productivity of Tesla's Shanghai factory. However, Tesla's output reached almost ******* vehicles in the first two quarters of 2020. As the virus began to spread to the American continent, work at the U.S. factory in Fremont, California was stopped. The plant's reopening in May was met with criticism but contributed to the over ****** units that were produced in the second quarter of 2020. Tesla witnessed production growth in all subsequent quarters. The company's output level reached a new record in the fourth quarter of 2024. Leading the electric vehicle market Tesla produced over **** million vehicles in 2024, a *** percent decrease on the company's stellar 2023, which had been driven to a large extent by Model 3 and Model Y production and sales figures. The Tesla Model 3 was the world’s best-selling plug-in electric vehicle in 2020 and 2021. In 2024, it faced tough competition from other Tesla models, including the Model Y and the refreshed Model S Plaid, and came third in the bestseller ranking.

This dataset provides a detailed comparison of cars sold by 34 automobile companies in the Indian market. It includes essential specifications that are useful for market research, comparison tools, and machine learning applications.

🔍 Features Included: Brand: Car manufacturer (e.g., Maruti Suzuki, Hyundai, Tata, etc.) Car Name: Model name Price: Listed price in Indian Rupees (Lakh format) Rating: User/customer ratings (out of 5) Safety: Safety ratings (if available) Mileage (kmpl): Claimed fuel efficiency range Power (BHP): Horsepower or power output range Sales: Number of cars sold in year 2024

📈 Use Cases: Automobile market analytics Car recommendation engines Fuel efficiency and power prediction models Brand-wise trend analysis

📌 Notes: The dataset may have some missing values (e.g., Safety ratings). All data is publicly available from official sources, review sites, and market listings.

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.

MyAutoData

Actual dataset Location on Kaggle Contains data scrapped by MyAutoScrapper (Written in go)

Purpose

Since this kaggle dataset real car deals, placed by real humans with pictures, It can be used for real world Machine Learning(ML) or Machine Vision. Price predictions, image processing, machine vision etc.

Data structure

This dataset contains data.csv file, which has 100 000 car deal detail. Each row representing each deal. data.csv has 18 columns: - ID: Represents unique identifier for each entry, also for each id, there is a sub-folder in images respectively, which contains images for the given deal. ID is an integer starting from 0. - Manufacturer: A string identifying car manufacturer. - Model: A string identifying car model. - Year: An Integer for the car production year. - Category: A type of the vechile (Sedan, Cabriolet, etc.). - Mileage: An integer representing car mileage in kilometers. - FuelType: A Fuel type the car uses. - EngineVolume: A Floating point number, representing engine volume in litres. - DriveWheels: A String representing car drive wheels (i.e. Front, Rear, 4x4, etc.). - GearBox: A string to identify gear box of the transmission (Manual, Automatic, etc.) - Doors: A string representing car doors (4, 4/5, etc.) - Wheel: Steering wheel position (Left Wheel, Right Wheel) - Color: Color of the car body. - InteriorColor: Interior color. - VIN: VIN number of the vechile, represented as a string. - LeatherInterior: A boolean value, true if car has a leather interior. - Price: Price of the car in USD. If ommited, meants price was set as negotiable. - Clearance: A boolean value identifying, whether customs has been cleared of not.

Important Note! (Disclaimer)

None of the fields (Except ID) are guaranteed to be filled, or filled with correct information. Since, people sometimes don't enter correct information, or hide some information for reasons. But for most of the entries, most of the fields are supposed to be filled with correct information.

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.

Five datasets are included here. The largest is the first and last year of production for American car models from 1896 to 2014. This is the dataset to be used with the PyRate program to estimate diversification dynamics. Fields in this dataset include: clade, a unique identifier for the auto manufacturer; species, a unique identifier for the car model; ts, the year of model origination; and te, the year of model extinction. Three covariate datasets are also included: raw GDP from 1896 to 2014, the price of oil (adjusted for inflation) from 1896 to 2014, and the total number of American car models during the same time period. All datasets are scaled so that the most recent time period is 0 (2014). Finally, a dataset that includes the first and last years of production for battery electric and hybrid electric vehicles is also provided, organized in the same structure as the American car models.

This dataset contains information about various cars and their specifications, with a focus on key attributes that are relevant for predicting car prices, drag race performance, and similar automotive-related predictions. The data is derived from a car website and may be updated in the future with additional performance-related information. Below is a description of each column in the dataset:

brand: The brand or manufacturer of the car. car_id: An identifier for each car in the dataset. model: The model or name of the car. cylinders: The number of cylinders in the car's engine. transmission: The type of transmission (e.g., automatic, manual). drive_wheel: The type of drive wheel configuration (e.g., front-wheel drive, rear-wheel drive, all-wheel drive). power: The car's power output in horsepower (HP). max_power_rpm: The RPM (revolutions per minute) at which the maximum power is achieved. torque: The car's torque output in Newton-meters (Nm). max_torque_rpm: The RPM at which the maximum torque is achieved. turbo: Indicates whether the car has a turbocharger and which type of turbocharger. fuel: The type of fuel the car uses (e.g., gasoline, diesel). top_speed: The car's maximum attainable speed in kilometers per hour (km/h). acc_0_100: The time it takes for the car to accelerate from 0 to 100 km/h in seconds. gear_1 to gear_9: Information about the gear ratios for each gear (if applicable). gear_r: Information about the reverse gear (if applicable). gear_final: The final drive ratio of the car's transmission. front_tire: Specifications of the front tires. rear_tire: Specifications of the rear tires. eng_capacity: The engine capacity in cubic centimeters (cc). weight: The weight of the car in kilograms (kg). height: The height of the car in millimeters (mm). width: The width of the car in millimeters (mm). length: The length of the car in millimeters (mm). wheelbase: The wheelbase of the car in millimeters (mm).

This dataset is well-suited for various predictive modeling tasks, including:

Car Price Prediction: The dataset provides key features like brand, model, engine specifications, and more, making it suitable for predicting car prices.

Drag Race Performance Prediction: With attributes such as power, torque, and acceleration data, this dataset can be used to predict a car's performance in drag races.

Automotive Analytics: Researchers and enthusiasts can use this dataset to conduct in-depth analysis of various car attributes and their impact on performance and pricing.

Recommendation Systems: The dataset can be used to build recommendation systems for car buyers based on their preferences and needs.

Machine Learning Projects: It serves as a valuable resource for machine learning projects related to cars, automotive technology, and performance analysis.

Keep in mind that as the dataset is updated with more performance-related data in the future, its utility for predicting various automotive-related outcomes is likely to increase.

Total Vehicle Sales in China increased to 2686000 Units in May from 2590000 Units in April of 2025. This dataset provides - China Total Vehicle Sales- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Being one of the largest automotive sectors, India had over 326 million registered vehicles as of financial year 2020. It was the largest producer of two-wheelers across the globe in 2023. The market within the country was also dominated by this segment. In financial year 2024, over 17.97 million units of two-wheelers were sold domestically across the south Asian country. A decline in the sales volume of two-wheelers has been witnessed between 2020 and 2022. Hero MotoCorpHero MotoCorp had the maximum share in the two-wheeler segment in India. The company was the worldwide leader in two-wheeler manufacturing. The company has taken up the initiative of manufacturing electric scooters and bikes. To reduce the high battery costs that create a significant cost difference between the petrol and the battery variants, the Indian government has introduced the National Programme on Advanced Chemistry Cell (ACC) in 2022 to inventivize batery manufacturing. Two-wheeler market outlookThe Indian government has set a target to electrify a major proportion of the two-wheelers within the nation. However, the manufacturers have encouraged the government to adopt more ‘realistic’ expectations, as the former’s scheme would mean the electrification of over two million vehicles. With the two-wheeler industry estimated to grow at over nine percent in the next few years, more investments in the clean energy sector could pave a way for the domestic market.