The Comprehensive Vehicle Make and Model Dataset provides a detailed list of automotive manufacturers and their corresponding models. This dataset includes data on various car makes (manufacturers) and models (specific car names under each make), making it ideal for use in automotive research, machine learning projects, or data enrichment tasks related to the automotive industry.

Dataset Features: Make: The name of the car manufacturer (e.g., Toyota, Ford, BMW). Model: The specific car model associated with each manufacturer (e.g., Camry, F-150, X5).

This dataset is structured to be easily accessible for relational databases, making it suitable for building relational models where car makes are linked to their models. It is especially useful for tasks like recommendation systems, market analysis, trend analysis, or training machine learning models that require automotive industry data.

Use Cases: Recommendation Engines: Develop systems that recommend car models based on user preferences. Market Research: Analyze the popularity or trends in specific car makes and models. Data Enrichment: Enrich datasets with car make and model information for enhanced data quality.

Data Structure: Each entry in the dataset consists of: Make: Manufacturer name. Models: List of car models associated with that make.

Are you searching for a comprehensive car model database? Look no further—Cars.com offers an extensive database of car makes and models, featuring detailed information to meet a wide range of needs. This rich resource includes data on make, model, year, specifications, pricing, features, and much more.

Whether you're an automotive business, a market researcher, or a developer building innovative car-related applications, this data of cars is an invaluable asset for performing in-depth vehicle analysis and trend forecasting.

What’s Included in the Vehicles Data:

• Car Models Database: Gain detailed insights into manufacturers' various car models, from compact cars to luxury sedans.
• Year: Access manufacturing year data to analyze trends in new releases and vintage classics.
• Specifications: Delve into technical details like engine types, horsepower, fuel efficiency, and transmission options.
• Pricing: Leverage current and historical pricing data to compare values and analyze market trends.
• Features: Explore safety features, entertainment systems, and comfort upgrades to evaluate vehicle appeal.
• Reviews and Ratings: Tap into customer and expert reviews to understand real-world vehicle performance and satisfaction.

This car datasets collection is regularly updated to provide the most accurate and reliable information. Whether you're developing an app, conducting market research, or simply staying informed about the latest trends, this car models database is your go-to resource for reliable vehicle data.

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Don’t miss out on this opportunity to elevate your projects with a robust database of car makes and models. Visit Crawl Feeds today and explore the full potential of this unparalleled resource.

The Car Model Variants and Images Dataset is a comprehensive collection of around 193k images across 3778 car model variants, obtained entirely through web scraping of the autoevolution.com website. Each model variant contains between 20 and 200 images in the size of 512x512, offering a diverse range of high-quality images that have been collected from a single reliable source.

The accompanying .csv file contains 44 columns of information about the car and the images that belong to them, making it easy to access and utilize the data. The information in the .csv file includes make, model, year, body type, engine type, transmission, and fuel type, among other specifications. Additionally, the file includes information on the image filenames and directories, providing quick access to the corresponding image data.

Some images might be missing due to being deleted as a bad format after resizing. However, despite the missing images, this dataset still provides a rich and diverse collection of car images that can be used for various machine learning tasks, such as image classification, object detection, and segmentation.

In conclusion, the Car Model Variants and Images Dataset is a reliable and comprehensive collection of high-quality car images and associated metadata, obtained through web scraping of the autoevolution.com website. The dataset is well-suited for use in a wide range of machine learning tasks, making it a valuable resource for researchers and practitioners in the computer vision field.

About this Dataset

This list includes all makes, models, years, and trim specifications for the fifty one makes listed below. Includes makes that were released after the year 2000 up until now. Trim data includes unique specifications such as engine data, AWD/FWD/2WD, and additional technology packages just to name a few. Useful data for populating databases, programming that requires accurately identifying/defining a car, and many other purposes. Other websites would typically sell data like this for >100$ (go check for yourself!!). Car Makes Included: Acura, Alfa Romeo, Am General, Audi, BMW, Buick, Cadillac, Chevrolet, Chrysler, Daewoo, Dodge, FIAT, Fisker, Ford, GMC, Genesis, Honda, Hummer, Hyundai, INFINITI, Isuzu, Jaguar, Jeep, Kia, Land Rover, Lexus, Lincoln, Lotus, Maserati, Maybach, Mazda, Mercedes-Benz, Mercury, MINI, Mitsubishi, Nissan, Oldsmobile, Panoz, Plymouth, Pontiac, Porsche, RAM, Saab, Saturn, Scion, smart, Subaru, Suzuki, Toyota, Volkswagen, Volvo

Category

Product Lists

Keywords

cars,automotive,auto,vehicle

Row Count

45888

Price

$56.00

The Car Prices dataset contains detailed information about various car models, including their manufacturing year, make, model, trim, body type, transmission, and state of condition. With over 550,000 entries, this dataset is an excellent resource for exploring trends in car prices, analyzing market value fluctuations, and developing predictive models for the automotive industry.

Features

1. Year: Manufacturing year of the vehicle (Range: 1982-2015).
2. Make: Brand of the car (Top makes: Ford - 17%, Chevrolet - 11%).
3. Model: Specific car models, including Altima (3%), F-150 (3%), and others.
4. Trim: Vehicle trim level or variant (Base - 10%, SE - 8%).
5. Body: Body type, such as Sedan (36%), SUV (21%), etc.
6. Transmission: Type of transmission (85% Automatic, 12% unknown).
7. VIN: Vehicle Identification Number, unique to each car.
8. State: Location where the vehicle is registered (e.g., CA - 13%, FL - 15%).
9. Condition: Condition score of the car (1–5 scale).
10. Odometer: Mileage covered by the car (Range: 1 to 1,000,000+ miles).

Use Cases

• Price Prediction: Build machine learning models to predict car prices based on features like make, model, year, and mileage.
• Market Analysis: Identify trends in car preferences and sales across different regions.
• Depreciation Study: Study how different brands and models lose value over time.

Statistics Overview

• Years Distribution: The dataset spans from 1982 to 2015, with most entries concentrated in the 2000s.
• Popular Brands: Ford and Chevrolet dominate the entries.
• Body Type Distribution: Sedans and SUVs make up over 50% of the dataset.
• Condition Scores: Vehicles with a condition score of 4-5 are most common.

Example Records

License

This dataset is available under the MIT License, making it suitable for both commercial and non-commercial use.

Tags

• Car Pricing
• Automotive Market
• Machine Learning
• Price Prediction
• Data Analysis

Download Now and explore the intricacies of car prices with this rich and diverse dataset!

Alesco Data's Automotive records are updated monthly from millions of proprietary sourced vehicle transactions. These incoming transactions are processed through compilation rules and are either added as new, incremental records to our file, or contribute to validating existing records.

Our recent focus is on compiling new vehicle ownership, and the file includes over 14.2 million late model vehicle owners (2020-2025).

In addition, we append our Persistent ID, telephone numbers, and demographics for a complete file that can support your direct mail and email marketing, lead validation, and identity verification needs. A Persistent ID is assigned to each vehicle record and tracks consumers as they change addresses or phone numbers, and vehicles as they change owners.

The database is not derived from state motor vehicle databases and therefore not subject to the Shelby Act also known as the Driver's Privacy Protection Act (DPPA) of 2000. The data is deterministic and sources include sales and service data, warranty data and notifications, aftermarket repair and maintenance facilities, and scheduled maintenance records.

Fields Included: -Make -Model -Year -VIN -Vehicle Class Code (crossover, SUV, full-size, mid-size, small) -Vehicle Fuel Code (gas, flex, hybrid) -Vehicle Style Code (sport, pickup, utility, sedan) -Mileage -Number of Vehicles per Household -First seen date -Last seen date -Email

Vehicle Make, Model, and Color Database Market Outlook

According to our latest research, the global Vehicle Make, Model, and Color Database market size reached USD 2.1 billion in 2024. The market is expected to grow at a CAGR of 7.3% during the forecast period, reaching a value of USD 3.9 billion by 2033. This robust growth is driven by the increasing digitization of automotive data, the proliferation of connected vehicles, and the heightened demand for real-time vehicle information across multiple industries. As per our latest research, the integration of advanced analytics and artificial intelligence within vehicle databases is further accelerating market expansion, enabling more precise and actionable insights for end-users globally.

The primary growth factor for the Vehicle Make, Model, and Color Database market is the escalating need for accurate and comprehensive vehicle information across diverse sectors. Automotive dealerships, insurance companies, and law enforcement agencies are increasingly relying on these databases to streamline operations, enhance customer experience, and improve decision-making processes. The rise in vehicle thefts, fraudulent insurance claims, and the need for efficient fleet management solutions have all contributed to a surge in demand for reliable vehicle data. Furthermore, the growing trend toward digital transformation within the automotive industry has led to the adoption of sophisticated database solutions, which offer seamless integration with existing IT infrastructures and ensure data accuracy and security.

Another significant growth driver is the rapid advancement in data collection technologies and the expanding sources of vehicle-related data. The proliferation of IoT-enabled vehicles, telematics, and connected car platforms has resulted in an exponential increase in the volume and variety of vehicle data available for analysis. This has enabled database providers to offer more granular and up-to-date information, catering to the specific requirements of end-users such as automotive manufacturers, government agencies, and transportation companies. The integration of machine learning and big data analytics further enhances the value proposition of these databases, enabling predictive insights and real-time data validation that support critical business functions and regulatory compliance.

The market is also witnessing increased collaboration between original equipment manufacturers (OEMs), aftermarket players, and technology providers to standardize and enrich vehicle data. These partnerships are essential for ensuring data consistency, interoperability, and scalability across different platforms and geographies. The adoption of cloud-based database solutions has further democratized access to vehicle data, allowing small and medium enterprises (SMEs) to leverage sophisticated analytics without significant upfront investments. Additionally, regulatory initiatives aimed at improving road safety and vehicle traceability are fueling the demand for comprehensive and up-to-date vehicle databases, particularly in emerging markets where vehicle ownership is on the rise.

From a regional perspective, North America continues to dominate the Vehicle Make, Model, and Color Database market, accounting for the largest share in 2024. This is attributed to the region's mature automotive ecosystem, high vehicle penetration, and early adoption of advanced data management technologies. Europe follows closely, driven by stringent regulatory requirements and a strong focus on vehicle safety and compliance. The Asia Pacific region is poised for the fastest growth during the forecast period, supported by rapid urbanization, increasing vehicle sales, and significant investments in digital infrastructure. Latin America and the Middle East & Africa are also emerging as promising markets, with growing awareness of the benefits of robust vehicle data management systems and the expansion of automotive and transportation sectors.

Database Type Analysis</h2

The NHTSA Vehicle Crash Test Database contains engineering data measured during various types of research, the New Car Assessment Program (NCAP), and compliance crash tests. Information in this database refers to the performance and response of vehicles and other structures in impacts. This database is not intended to support general consumer safety issues. For general consumer information please see the NHTSA's information on buying a safer car.

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.

VentiveIQ's auto owner profiles are built from a range of sources, including sales and service data, automobile warranty data, aftermarket repair and maintenance facilities, auto warranty notification, and scheduled maintenance records. Each record is matched with a Persistent ID, Household ID, and demographic data to facilitate precise targeting.
Empower your marketing efforts with potent auto ownership data that enables you to reach vehicle owners at the most critical decision-making period.

🚗 2025 Used Car Market Dataset 🚗 This dataset is carefully prepared for data scientists, analysts, and researchers who want to analyze the 2025 used car market. With approximately 2,500 rows and 13 different features, this dataset serves as a powerful resource for exploring pricing trends, brand-model preferences, and vehicle history.

📊 Dataset Contents:

price → Vehicle price brand → Brand model → Model year → Manufacturing year mileage → Mileage information title_status → Vehicle title status (Clean, Salvage, etc.) color → Color information vin, lot → Vehicle identification details 🎯 Use Cases: ✔️ Machine learning projects – Price prediction, regression models ✔️ Data analysis & visualization – Analyzing market trends ✔️ Used car market research

🔹 This dataset is clean, well-structured, and ready for use—start your analysis right away! We’d love to hear feedback from the Kaggle community. 🚀

👉 Let’s explore this data and uncover valuable insights together! 💡

Vehicle Make Model and Color Database Market Outlook

According to our latest research, the global Vehicle Make Model and Color Database market size in 2024 is valued at approximately USD 1.78 billion. The market is poised for robust expansion, exhibiting a compound annual growth rate (CAGR) of 11.2% from 2025 to 2033. By the end of 2033, the market is projected to reach USD 4.73 billion. This growth is primarily driven by the rising need for advanced vehicle identification systems across diverse sectors, including automotive, insurance, and law enforcement, as organizations increasingly prioritize data-driven operations and regulatory compliance.

One of the primary growth factors fueling the Vehicle Make Model and Color Database market is the surge in digitization within the automotive sector. The proliferation of connected vehicles, IoT-enabled fleet management solutions, and the widespread adoption of smart city initiatives have led to a substantial increase in the volume and complexity of vehicular data. Automotive dealerships, insurance providers, and fleet management companies are leveraging these databases to streamline their operations, improve customer service, and enhance security protocols. The ability to quickly and accurately identify vehicles by make, model, and color is becoming indispensable for managing inventories, processing insurance claims, and maintaining regulatory compliance. This digitization trend is expected to intensify as more organizations recognize the value of comprehensive, real-time vehicle data.

Another significant driver is the escalating demand for robust vehicle identification systems by law enforcement agencies and governmental bodies. The rise in vehicle-related crimes, coupled with the need for efficient traffic management, has compelled authorities to invest in advanced database solutions. These databases enable law enforcement agencies to rapidly identify stolen or suspicious vehicles, support automated license plate recognition systems, and contribute to the overall safety and security of urban environments. Furthermore, the integration of artificial intelligence and machine learning algorithms into these databases enhances their accuracy and predictive capabilities, allowing for proactive threat detection and incident response. As public safety concerns continue to mount, the adoption of vehicle make, model, and color databases by the public sector is expected to grow steadily.

The expansion of the global automotive aftermarket also plays a pivotal role in the growth of the Vehicle Make Model and Color Database market. As the average vehicle lifespan increases and the demand for used vehicles rises, accurate and up-to-date vehicle information becomes crucial for dealerships, car rental services, and insurance companies. These organizations rely on comprehensive databases to verify vehicle histories, assess risk profiles, and optimize pricing strategies. Additionally, the increasing popularity of online vehicle marketplaces and digital sales platforms further amplifies the need for reliable and easily accessible vehicle data. This trend is likely to persist as consumers and businesses continue to favor digital channels for vehicle transactions and management.

Regionally, North America currently dominates the Vehicle Make Model and Color Database market, accounting for a significant share of global revenue in 2024. The region’s leadership is attributed to its advanced automotive ecosystem, high penetration of digital technologies, and strong presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing vehicle ownership, and government initiatives aimed at modernizing transportation infrastructure. Europe also remains a critical market, benefiting from stringent regulatory standards and a mature automotive industry. Collectively, these regional dynamics underscore the global nature of the market and highlight the diverse opportunities for stakeholders across different geographies.

Database Type Analysis

The Vehicle Make Model and Color Database market is segmented by database type into structured, unstructured, and hybrid databases. Structured databases, which utilize a predefined schema and organized data models, remain the dominant segment due to their reliability, ease of integration, and compatibility with existing enter

The fields available include make, model, year, trim, style, fuel type, MSRP, and many more.

We have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used. This file contains over 180 million records in addition to over 1 million+ fresh automotive intender records per day.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

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.

Inventory Search API - Search inventories to access detailed vehicle breakdowns, including photos, prices, locations and installed equipment. - $0.2 per 100 calls

VIN History API - Enter a 17 digit VIN to see the price history, changing odometer readings & full details about each car for up to six years back. - $0.6 per 100 calls

Dealer API - Check inventories by unique dealer ID or locate and view inventories for multiple dealerships in geographical areas. - $0.25 per 100 calls

Enhanced Vin Decoder - Submit a 17 digit VIN to pull back year, make, model and trim with the installed equipment and detailed vehicle specs. - $8.0 per 100 calls

CRM Cleanse API - Use our data to clean prospect lists. Track any car's appearance online by 17 digit VIN and know when it has been sold. - $0.8 per 100 calls

Cars Market APIs - Get Market Days Supply value for a car - $0.6 per 100 calls

Private Party Inventory Search API - Search private seller listings by any combination of layered criteria across the US and Canada. - $1.0 per 100 calls

Auction Inventory Search API - Search auction listings by any combination of layered criteria across the US and Canada. - $0.8 per 100 calls

Dealer Recent Inventory Search API - Search by any combination of criteria to view any dealership's inventory from the last 90 days across the US and Canada. - $0.6 per 100 calls

Dealer Active Inventory Search API - Fetch dealers active inventory - $20.0 per 100 calls

Inventory Ranking API - Perform ranked search with dynamic rank criteria - $0.8 per 100 calls

OEM Incentive Search API - Search Incentive Programs for 30+ car manufacturer at one place - $0.6 per 100 calls

Cached Images API - Get a cached version of the car images of the photos listed on the VDP on the dealer's website. - $0.1 per 100 calls

Recall Lookup By VIN - Get open recall information for a VIN from multiple OEM microsites - $0.3 per 100 calls

------ Heavy Equipment APIs ------ Inventory Search API - Search used heavy equipment listings by layered criteria to get prices, options, photos, videos and equipment details. - $0.2 per 100 calls

Dealer API - Search individual dealerships or groups of dealers to see their inventories and dealer profiles. - $0.25 per 100 calls

------ Motorcycles APIs ------ Inventory Search API - Search motorcycle listings by layered criteria to get prices, options, photos, videos and equipment details. - $0.2 per 100 calls

Dealer API - Search individual dealerships or groups of dealers to see their inventories and dealer profiles. - $0.25 per 100 calls

------ Recreational Vehicles APIs ------ Inventory Search API - Search recreational vehicle listings by layered criteria to get prices, options, photos, videos and equipment details. - $0.2 per 100 calls

Dealer API - Search individual dealerships or groups of dealers to see their inventories and dealer profiles. - $0.25 per 100 calls

Key information about United States Motor Vehicle Production

• United States Motor Vehicle Production was reported at 10,562,188.000 Unit in Dec 2024.
• This records a decrease from the previous number of 10,639,140.000 Unit for Dec 2023.
• US Motor Vehicle Production data is updated yearly, averaging 7,762,544.000 Unit from Dec 1997 to 2024, with 28 observations.
• The data reached an all-time high of 13,024,978.000 Unit in 1999 and a record low of 5,731,397.000 Unit in 2009.
• US Motor Vehicle Production data remains active status in CEIC and is reported by International Organization of Motor Vehicle Manufacturers.
• The data is categorized under World Trend Plus’s Association: Automobile Sector – Table RA.OICA.MVP: Motor Vehicle Production: by Country.

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.

This Dataset contains state, vehicle maker, vehicle category and vehicle type wise total number of vehicles registered in India in each year

Notes: 1. The data is sourced from the Vahan Portal and reflects the last update done by us. Data is updated/refreshed once every month. Any mismatches are due to frequent updates on the Vahan dashboard. 2. For datasets with year-wise data, the data of the previous years is as of 31 December of that year whereas the data of the current year is as of date.