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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Power up your Electric Vehicle (EV) development with Datatorq's extensive data. Our curated database features over 250 data points, including key information like price, specs, features, and dimensions. Stay ahead of the curve with our regularly updated data.

Why choose Datatorq's Electric Vehicle (EV) Data? - EV-Focused Insights: Get the in-depth information you need to develop and price your EVs for optimal market success. - Granular View of the Evolving EV Landscape: Our EV specialists curate our data to provide unparalleled insights into the dynamic world of Electric Vehicles (EVs). - Confidence-Building Electric Vehicle (EV) Data: Our data is cleaned, comprehensive, and meticulously maintained to ensure the highest level of accuracy for your critical decisions. - Scalable & Customizable Solutions: We tailor our EV Data to your specific needs so you get exactly the data that drives results. - Always Up-to-Date Electric Vehicle (EV) Data: Our Electric Vehicle data is constantly refreshed and validated monthly, keeping you ahead of the curve in this fast-paced market.

Datatorq's expansive and precise Electric Vehicle (EV) datasets are designed to empower innovation and success in your EV product development and pricing strategies across Europe. Gain a competitive edge in France, UK, Italy, Poland, Netherlands, Spain, Belgium, Germany, Austria, Czechia, Portugal, Romania, Switzerland, Denmark, Norway, Slovenia, Sweden, and Ireland.

The CVS Database provides a catalogue of original vehicle dimensions, for use in vehicle safety research and collision investigation. The purpose of this database is to provide users with a comprehensive listing of vehicle dimensions commonly used in the field of collision investigation and reconstruction, for the North American fleet of passenger cars, light trucks, vans and SUV’s. The database includes model years dating back to 2011 and is comprised of both commonly available dimensions such as overall length, wheelbase and track widths, and also several dimensions which are not typically readily available from the manufacturers, nor from automotive publications. Note – To obtain database of model years dating back to 1971, please contact Transport Canada.

Datatorq's LCV Car Data powers more informed product development, providing 250+ data points—including pricing, features, specifications, and dimensions—all meticulously refined and updated monthly for a winning LCV strategy.

Why Choose Datatorq's Car Data? - Built for Product Development: Access key insights to refine your LCV product and pricing strategies. - Unparalleled Detail: Explore the LCV market in depth with data curated by industry specialists. - Reliable Quality: Accurate, clean, and complete—data you can depend on. - Flexible & Scalable: Customized to fit your unique requirements. - Continuously Updated: Verified and refreshed monthly to keep you current.

Datatorq's extensive and precise LCV Car Data datasets are designed to drive innovation and success in product and price strategies across Europe (France, UK, Italy, Poland, Netherlands, Spain, Belgium, Germany, Austria, Czechia, Portugal, Romania, Switzerland, Bulgaria, Croatia, Denmark, Hungary, Norway, Slovenia, Sweden, Ireland).

Datatorq's Product Data provides valuable insights to refine and elevate your product and pricing strategy.

Product Data is crucial for various applications in the automotive industry, such as: - Product Development - Pricing Strategy - Market Analysis - Competitor Benchmarking - Total Cost of Ownership (TCO)

Why Choose Our Product Data: - Meticulously Clean and Accurate: Trusted data, carefully refined for reliability. - Deep, Expert-Level Insights: Comprehensive LCV sector analysis, crafted by specialists. - Customizable and Scalable: Adapted to meet your unique requirements and goals. - Updated Monthly: Regularly validated for current, precise insights.

Regular updates for Latin America: Argentina, Brazil, Colombia and Mexico.

Empower your automotive business with our rich and accurate datasets, tailored to spark innovation and drive success in your product and pricing strategies.

⚡ Electric Vehicle Specs Dataset 2025

This dataset provides a comprehensive collection of specifications and performance metrics for modern electric vehicles (EVs), scraped from EV-Database.org. It supports use in:

🔍 Data science 🧠 Machine learning 📈 Market analysis ♻️ Sustainability studies 🚗 EV adoption research

  📊 Core Attributes

Each row represents a specific EV model with attributes across:

  🏷 Brand & Classification

Brand & Model:… See the full description on the dataset page: https://huggingface.co/datasets/UrvishAhir1/Electric-Vehicle-Specs-Dataset-2025.

Dataset

This dataset was created by Harsh Gheewala

Released under ODC Public Domain Dedication and Licence (PDDL)

Contents

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

Problem Statement

The noise level of cars could be an indicator of both car’s condition and manufacturing quality. Drivers could use noise level to determine if a potential car suits their needs, or if their current car is in a healthy state. On the other hand, manufactures could use noise level to assess their cars' quality compared to the market. Luxury cars compete to have low noise level, while sports car usually neglect this factor. In this project we compile data from different sources to arrive to a dataset having cars’ manufacturing specification mapped to its noise level at different speed. The compiled dataset could be utilized in evaluating cars noise level or in analyzing which technical specification has the major effect on cars’ noise level.

For mor information about automobile noise level:

• An overview of automobile noise and vibration control

• Noise, vibration, and harshness From Wikipedia

Executive Summary

Initially we scrape data from https://www.auto-decibel-db.com (hereafter referred AD). This website has nearly 2000 data entries about cars' cabin noise level. Each car in the website has its cabin noise (measured in decibel) at different speed. The website doesn't provide further information about the source or the methodology of its collected data, yet it's the most comprehensive data about the subject I could found. Another source which might be used for verification can be found at https://www.edmunds.com. While edmunds.com states its methodology of collecting noise level, its dataset is embedded in PDF files and is not comprehensive compared to the former.

After scrapping the noise level of cars, we use the available information we have about each car to find its specification. In the scrapped dataset from AD there's 4 features which can be used to identify same car's specification in other datasets: brand, model, year, and spec. After looking up the Web for websites and APIs having detailed and comprehensive data about cars, we decided on http://www.carqueryapi.com API (hereafter referred CQA). Though it's not accurate for some cars, and it has different spelling from our AD, it's the most accessible data we could find. In this section we map each car in AD to its equivalence in cqa using the 3 features: brand, model, and year. We first specify the model_id in CQA and then we will use model_id to retrieve the full specification of the car. Due to the limitation imposed by caranddriver.com on the number of requests (60 requests), we used Tor bridge to alternate IP address.

Finally, we look up for the full specification of each car in CQA using its model_id. In this section we added 60 features of specification of nearly a 1000 car in AD. We refer to each feature pulled from CQA by a postfix added to its column name: '_cqa'. At the end we succeeded in getting specification of 1067 car out of 1895 in AD. We couldn’t find specification for all cars in AD due to either different naming of cars between AD and QC, or the car doesn’t exist in QC.

Web scrapping sources and API

auto-decibel-db.com: This website has nearly 2000 data entries about cars' cabin noise level. Each car in the website has its cabin noise (measured in decibel) at different speed.

carqueryapi.com: a JSON based API for retrieving detailed car and truck information, including year, make, model, trim, and specifications. It has 73419 vichle in its database.

Open questions to explore

What technical specification have the major effect on car noise level? What are the patterns observed on car noise level through years, brands, and specification? Can we use car noise level to indicate car's brand, year, specification?

Dataset Description: Vehicle Specifications and Pricing

Overview:

This dataset contains information about various vehicle models, including their pricing, engine specifications, fuel efficiency, and dimensions. It can be useful for automobile analysis, price prediction, fuel efficiency studies, and vehicle classification.

Columns Explanation

name (or car_model) –> The full name of the car, including make and model. sports_car –> Boolean (TRUE/FALSE), indicating whether the vehicle is classified as a sports car. suv –> Boolean, indicating if the car is an SUV. wagon –> Boolean, indicating if the car is a wagon. minivan –> Boolean, indicating if the car is a minivan. pickup –> Boolean, indicating if the car is a pickup truck. all_wheel –> Boolean, indicating if the vehicle has all-wheel drive. rear_wheel –> Boolean, indicating if the vehicle has rear-wheel drive. msrp –> Manufacturer’s Suggested Retail Price (in dollars). dealer_cost –> The cost a dealer pays for the vehicle (in dollars). eng_size –> Engine size in liters. ncyl –> Number of cylinders in the engine. horsepwr –> Horsepower of the engine. city_mpg –> Miles per gallon (MPG) in city driving conditions. hwy_mpg –> Miles per gallon (MPG) in highway driving conditions. weight –> Vehicle weight (in pounds). wheel_base –> Distance between the front and rear axles (in inches). length –> Total vehicle length (in inches). width –> Total vehicle width (in inches).

Potential Uses of This Dataset

✅ Price Prediction – Predict vehicle prices based on specs. ✅ Fuel Efficiency Analysis – Compare city and highway MPG across models. ✅ Vehicle Classification – Identify SUVs, sports cars, minivans, etc. ✅ Market Analysis – Analyze pricing trends and dealer costs. ✅ Weight vs. Fuel Economy – Study the relationship between vehicle weight and efficiency.

Dataset Description:

This dataset contains scraped data from Cars24, a popular online marketplace for buying and selling used cars. It provides detailed information on various pre-owned car listings, including:

• Model Name and Manufacturing Year
• Price and KM Driven (mileage)
• Engine Capacity and Fuel Type
• Transmission Type (Manual/Automatic)
• Ownership History (First-hand, Second-hand, etc.)
• Availability of Spare Key
• Imperfections and Repainted Parts

This dataset can be useful for data analysis, machine learning models, and business insights related to used car pricing, depreciation trends, and market demand.

Potential applications include:
✔️ Price Prediction Models
✔️ Resale Value Analysis
✔️ Identifying Key Factors Affecting Car Prices
✔️ Customer Preference Insights

Whether you're a data scientist, analyst, or automotive enthusiast, this dataset provides valuable insights into the used car market. 🚗📊

This dataset provides a detailed view of various car models and their specifications, sourced from car sales advertisements. It includes information on car make, model, body type, origin, and drivetrain, along with key performance metrics like engine size, horsepower, fuel efficiency (MPG), and physical dimensions. Additionally, financial details such as Manufacturer's Suggested Retail Price (MSRP) and invoice pricing are provided, offering insight into market positioning and pricing trends across different types and origins of vehicles.

This dataset is ideal for exploratory data analysis (EDA), allowing users to uncover patterns, trends, and potential correlations within the automotive market, from vehicle performance to pricing strategies.

Technical information and appearance information of nearly 30,000 cars from 124 car companies.

This dataset contains 54 columns of information for each car. Information like the car's model, series, engine, gearbox, fuel consumption, dimensions, etc.

This dataset is useful for practicing data cleaning, NLP, and classification.

Vehicle Product Information Catalog with factory specifications, build-plant data and technical details for vehicles manufactured since 1981.

An API for car number plate lookups. Our API is a .NET ASMX webservice, that allows you connect from any programming environment, either .NET (C#, Visual Basic .NET), or through any programming language that supports SOAP (PHP nuSoap, Python, Ruby, Java etc.). Register for free and we will set you up with a test account, where you can make 10 vehicle lookups for free. If you require higher volume of lookups, then our fee is 2.10 Pakistani Rupee (USD $0.02) per lookup, purchased in blocks of a minimum of 1,000, a 10% discount is available for packages over 10,000. Credits bought in Pakistan cannot be used in other countries.

This data set is a collection of European cars data containing 25 columns and 6000 rows

This dataset provides comprehensive information on European market cars, including electric vehicles (EVs), hybrids, diesel, and petrol models.

It is designed for data science, machine learning, and automotive analytics applications such as car recommendation systems, range prediction, and comparative market studies.

The dataset includes over thousands of car records with detailed technical, economic, and safety-related specifications.

All data has been normalized and standardized for AI-ready analysis, ensuring consistency across electric and internal combustion engine (ICE) vehicles.

⚙️ Key Features

• 🧩 Complete technical specs— horsepower, torque, acceleration, efficiency, battery/range, top speed
• 🔋 Powertrain coverage — electric, hybrid, petrol, and diesel
• 💶 Economic data — purchase price, energy/fuel cost, and maintenance
• 🛡️ Safety & automation — ADAS level and Euro NCAP safety ratings
• 📊 AI-ready structure — clean schema for predictive modeling and recommendation engines

💡 Potential Use Cases

• Car recommendation systems for consumers or dealers
• Market comparison of EVs vs ICE vehicles
• Predictive modeling for price, range, or efficiency
• Automotive trend analysis and sustainability research
• AI/ML integration in automotive data platforms

🧭 Source & Provenance

The dataset was compiled and normalized from verified European automotive sources, including manufacturer specifications, EV-database.org, and automotive market listings. Values such as price, range, and performance metrics were cross-validated using multiple public references to maintain accuracy.

⚖️ License

Licensed under CC BY 4.0 (Attribution License) — you may use and modify this dataset with proper attribution.

📈 Update Frequency

This dataset is static, based on 2024 vehicle specifications. It may be updated quarterly as new European models are released.

🧰 Compatibility

File Format : .xlsx (Excel) Columns Described : ✅ Yes
License : ✅ CC BY 4.0
Language : English

Including Australian car registration lookups. Our API is a .NET ASMX webservice, that allows you connect from any programming environment, either .NET (C#, Visual Basic .NET), or through any programming language that supports SOAP (PHP nuSoap, Python, Ruby, Java etc.). Register for free and we will set you up with a test account, where you can make 10 vehicle lookups for free. If you require higher volume of lookups, then our fee is NZD $ 0.30 per lookup, purchased in blocks of a minimum of 100, a 10% discount is available for packages over 1000.

This layer shows household size by number of vehicles available. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Common Vehicle Signal Specification Services Market Outlook

According to our latest research, the Common Vehicle Signal Specification Services market size reached USD 1.68 billion in 2024, reflecting a robust trajectory fueled by the automotive industry’s digital transformation. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted market size of USD 4.79 billion by 2033. This accelerated growth is primarily driven by the increasing demand for standardized data exchange protocols, greater adoption of connected and autonomous vehicles, and the need for seamless integration across vehicle communication networks.

One of the primary growth drivers for the Common Vehicle Signal Specification Services market is the automotive sector's rapid embrace of digitalization and connectivity. As vehicles become increasingly sophisticated, the need for standardized signal specifications has become critical to ensure interoperability between various electronic systems and components. OEMs and suppliers are under mounting pressure to integrate advanced features such as ADAS, infotainment, and telematics, all of which require reliable and standardized communication channels. The proliferation of electric and autonomous vehicles further amplifies this need, as these vehicles depend on real-time data exchange to function safely and efficiently. As a result, the demand for consulting, integration, and support services surrounding vehicle signal specifications is on a steady rise, fueling market expansion.

Another significant factor contributing to market growth is the regulatory landscape. Governments and industry bodies across the globe are introducing stringent regulations mandating data standardization and cybersecurity within the automotive sector. These regulations are compelling automotive OEMs and their supply chain partners to invest in advanced Common Vehicle Signal Specification Services. Compliance with standards such as AUTOSAR and ISO 26262 is now a prerequisite for market entry, especially in regions like North America and Europe. This regulatory push is not only ensuring greater safety and reliability but also creating lucrative opportunities for service providers specializing in consulting, integration, and maintenance of vehicle signal specifications.

The rise of mobility-as-a-service (MaaS) and fleet management solutions is also acting as a catalyst for the Common Vehicle Signal Specification Services market. Fleet operators and mobility providers require seamless integration of various vehicle types and brands within their ecosystem. Standardized signal specifications enable efficient vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, which is essential for fleet optimization, predictive maintenance, and operational efficiency. As the shared mobility sector continues to expand, the reliance on robust specification services will only intensify, creating a positive feedback loop that supports long-term market growth.

From a regional perspective, Asia Pacific dominates the Common Vehicle Signal Specification Services market, driven by its burgeoning automotive manufacturing base and rapid adoption of next-generation vehicle technologies. North America and Europe follow closely, benefiting from strong regulatory frameworks and significant investments in connected and autonomous vehicle infrastructure. Meanwhile, Latin America and the Middle East & Africa are witnessing gradual uptake as local manufacturers and fleet operators recognize the benefits of standardized vehicle communication. This diverse regional landscape ensures that market growth remains balanced and sustainable over the forecast period.

Service Type Analysis

The Service Type segment of the Common Vehicle Signal Specification Services market is comprised of Consulting, Integration & Implementation, Support & Maintenance, and Training & Education. Consulting services account for a significant share, as automotive OEMs