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.

The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints.

This dataset contains information about car listings scraped from the Yad2 website in Israel. The data was collected on June 4th, 2024. It includes various attributes of the cars, such as their age, horsepower, fuel type, and more, which can be used for predicting car prices.

Dataset Features:

• brand: The brand of the car.
• model: The model of the car.
• car_name: The name of the car.
• price: The listed price of the car (NIS).
• year: The year the car was manufactured.
• hand_num: The number of previous owners.
• horse_power: The horsepower of the car.
• 4x4: Indicates whether the car is a 4x4 vehicle.
• fuel_type: The type of fuel the car uses (e.g., Petrol, Diesel).
• engine_volume: The volume of the car's engine.
• valid_test: Indicates whether the car has a valid test.
• magnesium_wheels: Indicates whether the car has magnesium wheels.
• distance_control: Indicates whether the car has distance control.
• economical: Indicates whether the car is economical.
• adaptive_cruise_control: Indicates whether the car has adaptive cruise control.
• cruise_control: Indicates whether the car has cruise control.
• brand_normalized: Normalized brand name.
• brand_group: Grouping of brands.
• log_price: The logarithm of the car's price.
• cluster: Cluster label assigned based on K-Means clustering of price and horsepower.
• cluster_label: Descriptive label for the cluster.
• car_age: The age of the car.
• electric_or_not: Indicates whether the car is electric.

Source: The data was scraped from the Yad2 website, a popular online marketplace in Israel.

Usage: This dataset can be used for various purposes, including:

• Predicting car prices based on their attributes.
• Analyzing trends in car prices and features.
• Clustering cars based on their characteristics.

Acknowledgements: Please acknowledge the Yad2 website as the source of the data if you use this dataset in your work.

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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.

United States Average Transaction Price: Category: Full-Size Car data was reported at 40,462.000 USD in Mar 2025. This records an increase from the previous number of 38,335.000 USD for Feb 2025. United States Average Transaction Price: Category: Full-Size Car data is updated monthly, averaging 44,227.000 USD from Jan 2020 (Median) to Mar 2025, with 63 observations. The data reached an all-time high of 49,410.000 USD in Oct 2023 and a record low of 36,905.000 USD in Jan 2020. United States Average Transaction Price: Category: Full-Size Car data remains active status in CEIC and is reported by Cox Automotive. The data is categorized under Global Database’s United States – Table US.RA011: New Vehicle Average Transaction Price.

The Danish SpeechDat-Car comprises the recordings of 300 Danish speakers from 5 different regions (162 males, 138 females), recorded over the GSM telephone network, and in a car. This database is partitioned into 15 DVDs (53 GB), plus 1 CD-ROM for e.g. non-signal files and documentation. The speech databases made within the SpeechDat-Car project were validated by SPEX, the Netherlands, to assess their compliance with the SpeechDat-Car format and content specifications.The speech data files are in two formats. Four of the microphones were recorded on the computer in the boot of the car. The speech data are stored as sequences of 16 kHz, 16 bit and uncompressed. The fifth microphone was connected to the cell phone, and was recorded on a remote machine, with compressed data stored as sequences of 8 bit A-law 8.kHz. Each signal file is accompanied by an ASCII SAM label file which contains the relevant descriptive information.Each speaker uttered the following items:2 voice activation keywords1 sequence of 10 isolated digits7 connected digits : 1 sheet number (5+ digits), 1 spontaneous telephone number, 3 read telephone numbers, 1 credit card number (14-16 digits), 1 PIN code (6 digits)3 dates : 1 spontaneous date (e.g. birthday), 1 prompted date, 1 relative or general date expression2 word spotting phrases using an application word (embedded)4 isolated digits7 spelled words : 1 spontaneous (own forename or surname), 1 spelling of directory city name, 4 real word/name, 1 artificial name for coverage1 money amount1 natural number7 directory assistance names : 1 spontaneous (own forename or surname), 1 city of birth / growing up (spontaneous), 2 most frequent cities, 2 most frequent company/agency, 1 "forename surname"9 phonetically rich sentences2 time phrases : 1 time of day (spontaneous), 1 time phrase (word style)4 phonetically rich words67 application words: 13 mobile phone application words, 22 IVR function keywords, 32 car products keywords2 additional language dependent keywordsPrompts for spontaneous speech2 additional keywords from a list of 10The following age distribution has been obtained: 84 speakers are between 18 and 30, 99 speakers are between 31 and 45, 98 speakers are between 46 and 60, and 19 speakers are over 60.A pronunciation lexicon with a phonemic transcription in SAMPA is also included.

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Snapshot img

US Used Car Market Size 2025-2029

The us used car market size is forecast to increase by USD 40.2 billion at a CAGR of 4.3% between 2024 and 2029.

The used car market in the US exhibits robust growth, driven by the excellent value proposition that pre-owned vehicles offer to consumers. This market trend is further bolstered by the increasing penetration of online platforms dedicated to selling used cars, providing greater convenience and accessibility for buyers. However, the market faces regulatory challenges as stricter emission regulations limit the sale of non-compliant used cars, necessitating investments in upgrading inventory and adhering to regulatory frameworks. These hurdles, while significant, can be navigated through strategic partnerships with emission testing centers and ongoing investment in fleet modernization. Companies that effectively address these challenges and leverage the opportunities presented by the growing demand for used cars and the digital shift in sales channels will thrive in this dynamic market.

What will be the size of the US Used Car Market during the forecast period?

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In the dynamic used car market, consumers face various challenges such as car scams and fraudulent activities. To mitigate risks, car buyers turn to comprehensive car buying guides and car detailing services. A VIN number check is essential for vehicle identification and history assessment, while emissions testing ensures environmental compliance. Car sharing and subscription services offer flexible mobility solutions. Vehicle registration and title transfer processes can be streamlined through digital means, and car refurbishment and connected car technology enhance safety and convenience. Blind spot monitoring and adaptive cruise control are popular safety features, while collision avoidance systems and lane departure warning systems provide added protection. Used car logistics and online financing applications simplify the purchasing process, and extended warranties offer peace of mind. Wireless charging, smartphone integration, and vehicle diagnostics are essential features for modern cars. Sustainable mobility and car comparison tools cater to eco-conscious consumers, while car maintenance schedules and roadside assistance ensure long-term vehicle care. Remote vehicle inspection and car care tips help maintain a car's resale value, and car subscription services offer flexible ownership alternatives. Used car fraud prevention and vehicle identification technologies protect buyers from potential risks. Car safety ratings and vehicle identification numbers are crucial tools for informed decision-making.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Distribution Channel3P channel salesOEM channel salesProductMid sizeFull sizeCompact sizeVendor TypeOrganizedUnorganizedFuel TypeDieselPetrolGeographyNorth AmericaUS

By Distribution Channel Insights

The 3p channel sales segment is estimated to witness significant growth during the forecast period.

The used car market in the US is a dynamic and significant sector, with numerous entities shaping its activity. Used car buyers continuously seek value, leading to a high demand for pre-owned vehicles. Search engine optimization and online advertising play crucial roles in connecting buyers with sellers, whether they're private parties or car dealerships. Wholesale car lots and auctions provide inventory for dealerships, ensuring a steady supply of used cars. Fleet vehicles, often traded in for newer models, contribute to the used car inventory. Maintenance records and vehicle history reports are essential for buyers, influencing their purchasing decisions. Safety features, infotainment systems, and driver assistance are increasingly desired in used cars, especially among budget-conscious consumers and luxury car buyers. Electric and hybrid vehicles are gaining popularity, driving the demand for used models in these categories. Car negotiation, fuel economy, and vehicle valuation are essential factors in used car selling. Digital marketing, including social media, mobile apps, and data analytics, helps sellers reach a wider audience. Certified pre-owned vehicles, reconditioned cars, and consignment sales offer buyers additional options and peace of mind. Car financing, vehicle inspections, and warranties are essential components of the used car buying process. Autonomous driving technology and car pricing trends continue to evolve, impacting the used car market. As the average ownership cycle shortens, the market will see an increase in the availability of used cars, making it an exciting and ever-changing landscape for both buyers and sellers.

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Comprehensive dataset of 1,774 Car manufacturers in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Gain valuable insights into the automotive market with our comprehensive Car Prices Dataset. Designed for businesses, analysts, and researchers, this dataset provides real-time and historical car pricing data to support market analysis, pricing strategies, and trend forecasting.

Dataset Features

Vehicle Listings: Access detailed car listings, including make, model, year, trim, and specifications. Ideal for tracking market trends and pricing fluctuations. Pricing Data: Get real-time and historical car prices from multiple sources, including dealerships, marketplaces, and private sellers. Market Trends & Valuations: Analyze price changes over time, compare vehicle depreciation rates, and identify emerging pricing trends. Dealer & Seller Information: Extract seller details, including dealership names, locations, and contact information for lead generation and competitive analysis.

Customizable Subsets for Specific Needs Our Car Prices Dataset is fully customizable, allowing you to filter data based on vehicle type, location, price range, and other key attributes. Whether you need a broad dataset for market research or a focused subset for competitive analysis, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Pricing Strategy: Track vehicle price trends, compare competitor pricing, and optimize pricing strategies for dealerships and resellers. Automotive Valuation & Depreciation Studies: Analyze historical pricing data to assess vehicle depreciation rates and predict future values. Competitive Intelligence: Monitor competitor pricing, dealership inventory, and promotional offers to stay ahead in the market. Lead Generation & Sales Optimization: Identify potential buyers and sellers, track demand for specific vehicle models, and enhance sales strategies. AI & Predictive Analytics: Leverage structured car pricing data for AI-driven forecasting, automated pricing models, and trend prediction.

Whether you're tracking car prices, analyzing market trends, or optimizing sales strategies, our Car Prices Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

• We inform you that the automobile comprehensive information disclosure service will be provided after the stabilization of the next-generation automobile management information system, and that new applications are currently not possible. (Expected after August 2025) * Details such as how to use and provided items will be announced later (Inquiries: 054-459-7492) ㅇ (Attached API) A service that searches for basic vehicle information (35 cases), specifications (70 cases), maintenance history (16 cases), and performance inspection (56 cases) by entering the vehicle registration number (vehicle number) * Additional open items: Includes chassis number, owner change history, and initial acquisition amount - If the owner consents to the provision of third-party information based on the vehicle registration number and owner name, the values of the items to be called are provided ㅇ (Deployment data) Data provided based on information on new (49 cases), transfer (57 cases), mortgage (58 cases), cancellation (49 cases), and structural change (47 cases) of automobiles that occur every month - Registration data provides each registration information from the 1st to the last day of each month in the following month - The data can be provided from November 2021 to the present.

ABOUT CAR PRICES MARKET

This dataset provides a comprehensive list of OLD and NEW car prices in the market, with information on various factors such as car make, year, model, transmission type, and more. With over 10,000 data points, this dataset allows for in-depth analysis and exploration of the dynamics of car prices in the market, making it a valuable resource for researchers, analysts, and car enthusiasts alike.

Content:

Used Cars Prices

Here you find 78612 records about used cars: 60 Brand, 382 Model, 33 Modelyear, 1839 CarModel, 1397 AveragePrice, 893 MinimumPrice, 916 MaximumPrice, over 128 Months/Year.

New Cars Prices

Here you find 3433 records about new cars: 1119 OldPrice, 410 ChangValue, 1162 NewPrice with 268 ChangeDate, on 49 Brand, 178 Model, over 4 Years

You can use this data to practice:

1. Data cleaning
2. Data Analysis
3. Data visualization
4. Machine Learning Price Forecasting

Inspiration About Dataset:

1- Price Prediction: The dataset contains information about various car models, such as their brand, model, year, fuel type, and transmission. This information can be used to predict the price of a car using regression models.

2- Brand Analysis: The dataset contains information about the brand of each car. You can analyze the dataset to see which brand has the highest average price.

3- Transmission Analysis: You can analyze the dataset to see how the price of a car varies with transmission type. For example, you can see if cars with automatic transmissions have a higher or lower price than cars with manual transmissions.

Question To Answer:

1. Which car brand has the highest average price?
2. Which fuel type has the highest average price?
3. How does the price of a car vary with its age?
4. Which transmission type is more popular among car buyers?
5. What is the distribution of car prices across different car brands?
6. Which car brand has the highest resale value?
7. How does the price of a car vary with its condition (i.e., new vs. used)?
8. Is there a relationship between the price of a car and its brand?
9. Which car brand has the highest rate of electric or fuel cars?
10. Can we predict the fuel efficiency of a car based on its features?

This dataset provides comprehensive details on used car listings, including vehicle specifications, features, pricing, and more. It's valuable for analyzing car prices, trends, and customer preferences in the automotive market.

Columns Description

• make_model: The brand and model of the vehicle (e.g., 'Audi A1').
• body_type: The body style of the vehicle, such as Sedan, Compact, or Station Wagon.
• price: The listed price of the car in currency.
• vat: Indicates the VAT status for the vehicle's price (e.g., VAT deductible, Price negotiable).
• km: The total mileage (in kilometers) of the vehicle, indicating its usage.
• Type: Condition of the vehicle, whether it's 'Used' or 'New'.
• Fuel: Type of fuel the vehicle uses, such as 'Diesel', 'Benzine', etc.
• Gears: The number of gears in the vehicle's transmission.
• Comfort_Convenience: Comfort and convenience features, such as 'Air conditioning', 'Leather steering wheel', 'Cruise control', and more.
• Entertainment_Media: Media features available in the vehicle, including 'Bluetooth', 'MP3', 'Radio', etc.
• Extras: Additional features like 'Alloy wheels', 'Sport suspension', etc.
• Safety_Security: Safety features like 'ABS', 'Airbags', 'Electronic stability control', 'Isofix', etc.
• age: Age of the car (calculated based on the model year).
• Previous_Owners: The number of previous owners the car has had.
• hp_kW: Engine power in kilowatts (kW), indicating the performance capacity of the engine.
• Inspection_new: Indicates whether the car has recently undergone an inspection (1 for yes, 0 for no).
• Paint_Type: The type of paint on the car, such as 'Metallic', 'Matte', etc.
• Upholstery_type: The material used for the interior upholstery, such as 'Cloth', 'Leather', etc.
• Gearing_Type: The type of transmission the car uses, either 'Automatic' or 'Manual'.
• Displacement_cc: The engine displacement in cubic centimeters (cc), indicating the size of the engine.
• Weight_kg: The total weight of the vehicle in kilograms.
• Drive_chain: The type of drivetrain, indicating whether it's 'Front' or 'Rear' wheel drive.
• cons_comb: The combined fuel consumption in liters per 100 kilometers.

Key Features

• Vehicle Specifications: Covers details like make, model, body type, fuel type, and more.
• Comfort & Safety Features: Includes information on air conditioning, safety features, and other convenience options.
• Performance Metrics: Provides data on mileage, engine power, weight, and fuel consumption.
• Pricing Information: Insights into vehicle pricing, VAT status, and other cost-related details.

Ideal Use Cases

• Price Prediction: Model car prices based on features like mileage, fuel type, and performance.
• Market Analysis: Explore trends and preferences in the used car market, by type, region, or other metrics.
• Customer Segmentation: Segment the dataset to analyze different customer preferences for car types, features, or price ranges.
• Feature Importance: Identify the most important factors influencing car prices (e.g., fuel type, mileage, age).

This dataset is ideal for machine learning, data analysis, and business intelligence applications in the automotive industry.

Comprehensive dataset of 96,058 Used car dealers in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Recent five years property insurance market any motor vehicle insurance premium income statistics - Self-use large passenger car (Bao Fa Center)

The Connected Car Market size was valued at USD 101.20 billion in 2023 and is projected to reach USD 244.05 billion by 2032, exhibiting a CAGR of 13.4 % during the forecasts period. This upswing is fueled by the increasing adoption of smart technologies, rising concerns over vehicle safety and security, and the escalating demand for personalized driving experiences. Government initiatives promoting connected car technologies further contribute to market expansion. Connected cars represent the integration of internet-connected technology into automobiles, revolutionizing transportation. These vehicles offer enhanced safety features, real-time navigation, and remote diagnostics. They facilitate seamless communication between vehicles, infrastructure, and external services, optimizing traffic flow and reducing congestion. Through IoT sensors and advanced analytics, connected cars gather data on driving patterns, allowing for personalized services and predictive maintenance. Recent developments include: In April 2023, CEREBRUMX announced a strategic investment from BlackBerry Limited to expand its portfolio of connected car data products. This agreement enables CerebrumX to broaden its data gathering and analyzing services while utilizing BlackBerry's cloud-connected automotive AI platform, IVY. CerebrumX can perform intense data processing instantly at the car edge by integrating BlackBerry IVY, providing real-time insights for automakers and other ecosystem suppliers. , In March 2023, Aeris acquired Ericsson's IoT Accelerator and Connected Vehicle Cloud businesses and related assets. This acquisition strengthens Aeris' Connected Vehicle business, allowing them to assist numerous Automotive OEMs in deploying, monetizing, and advancing their connected vehicle programs. By merging these businesses, Aeris has created one of the largest IoT-first connectivity management service platforms globally. Together with its network of partners, Aeris aims to deliver innovative IoT solutions that drive digital transformation, enhance operational efficiency, and enhance customer satisfaction for enterprises worldwide. , In January 2023, EPAM Systems, in partnership with Renesas Electronics Corporation, launched the "AosEdge" platform, a vehicle-to-cloud (V2C) solution that advances connected car development. This platform enables more efficient delivery of in-vehicle software and simplifies the operation of different software elements within the same environment. It empowers original equipment manufacturers (OEMs) to develop software-defined vehicles, combining EPAM's digital platform expertise with Renesas' embedded automotive software technology. The AosEdge platform provides a comprehensive software infrastructure for vehicles connecting to the cloud. , In May 2022, ECARX and COVA Acquisition Corp. announced a merger agreement. The firm specializes in developing hardware and software solutions for connected, automated, and electrified mobility to meet the changing needs of the worldwide automobile industry. ECARX is well-positioned to gain from the move to electric platforms, connected automobiles, and autonomous driving technology. ECARX can offer a full product roadmap and construct an automotive technology platform that effectively satisfies customer needs by collaborating strategically with original equipment manufacturers (OEMs) from the early phases of vehicle development. , In February 2022, Mojio launched an innovative program to assist automobile Original Equipment Manufacturers (OEMs) in planning to close 3G cellular networks. This closure will impact many American drivers and may cause essential telematics-based emergency services to be disrupted. Mojio has improved its 4G-connected car service and established the 4G Upgrade Program to address this. , In January 2022, Amazon.com, Inc. collaborated with Stellantis NV to transform the in-vehicle experience for multiple Stellantis clients and accelerate the automotive sector towards a software-driven economy. This collaboration aims to integrate Amazon Devices, Amazon Web Services (AWS), and Amazon Last Mile into Stellantis' operations. Stellantis NV plans to expedite its transformation into an environmentally friendly mobility technology company by embracing Amazon's technology and software expertise. The partnership will include various areas, such as vehicle development, connected in-car experiences, and training future generations of automotive software developers. .

The Car Allowance Rebate System (CARS), otherwise known as Cash for Clunkers, was a program intended to provide economic incentives to United States residents to purchase a new and more fuel efficient vehicle when trading in a less full efficient vehicle. The program was promoted as providing stimulus to the economy by boosting auto sales, while putting safer, cleaner and more fuel efficient vehicles on the road.

Comprehensive dataset of 30,656 Car accessories stores in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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.