Context

Dataset is about cars from back in 85. It's raw and messy.

Content

This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe. The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc...), and represents the average loss per car per year.

Note: Several of the attributes in the database could be used as a "class" attribute.

Number of Instances: 205

Number of Attributes: 26 total
-- 15 continuous
-- 1 integer
-- 10 nominal

Attribute Information:

| Attribute | Attribute Range

1. symboling: -3, -2, -1, 0, 1, 2, 3
2. normalized-losses: continuous from 65 to 256
3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo
4. fuel-type: diesel, gas
5. aspiration: std, turbo
6. num-of-doors: four, two
7. body-style: hardtop, wagon, sedan, hatchback, convertible
8. drive-wheels: 4wd, fwd, rwd
9. engine-location: front, rear
10. wheel-base: continuous from 86.6 120.9
11. length: continuous from 141.1 to 208.1
12. width: continuous from 60.3 to 72.3
13. height: continuous from 47.8 to 59.8
14. curb-weight: continuous from 1488 to 4066
15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor
16. num-of-cylinders: eight, five, four, six, three, twelve, two
17. engine-size: continuous from 61 to 326
18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi
19. bore: continuous from 2.54 to 3.94
20. stroke: continuous from 2.07 to 4.17
21. compression-ratio: continuous from 7 to 23
22. horsepower: continuous from 48 to 288
23. peak-rpm: continuous from 4150 to 6600
24. city-mpg: continuous from 13 to 49
25. highway-mpg: continuous from 16 to 54
26. price: continuous from 5118 to 45400.

Acknowledgements

"Automobile Data Set" from the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data.

Inspiration

I used these data for data cleaning/analysis purposes.

from: https://archive.ics.uci.edu/ml/datasets/car+evaluation

1. Title: Car Evaluation Database

2. Sources: (a) Creator: Marko Bohanec (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) Blaz Zupan (blaz.zupan@ijs.si) (c) Date: June, 1997

3. Past Usage:

   The hierarchical decision model, from which this dataset is derived, was first presented in

   M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78, 1988.

   Within machine-learning, this dataset was used for the evaluation of HINT (Hierarchy INduction Tool), which was proved to be able to completely reconstruct the original hierarchical model. This, together with a comparison with C4.5, is presented in

   B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition. ICML-97, Nashville, TN. 1997 (to appear)

4. Relevant Information Paragraph:

   Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates cars according to the following concept structure:

   CAR car acceptability . PRICE overall price . . buying buying price . . maint price of the maintenance . TECH technical characteristics . . COMFORT comfort . . . doors number of doors . . . persons capacity in terms of persons to carry . . . lug_boot the size of luggage boot . . safety estimated safety of the car

   Input attributes are printed in lowercase. Besides the target concept (CAR), the model includes three intermediate concepts: PRICE, TECH, COMFORT. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see http://www-ai.ijs.si/BlazZupan/car.html).

   The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety.

   Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods.

5. Number of Instances: 1728 (instances completely cover the attribute space)

6. Number of Attributes: 6

7. Attribute Values:

   buying v-high, high, med, low maint v-high, high, med, low doors 2, 3, 4, 5-more persons 2, 4, more lug_boot small, med, big safety low, med, high

8. Missing Attribute Values: none

9. Class Distribution (number of instances per class)

   class N N[%]

   unacc 1210 (70.023 %) acc 384 (22.222 %) good 69 ( 3.993 %) v-good 65 ( 3.762 %)

The Synset Boulevard dataset contains a total of 259,200 synthetically generated images of cars from a frontal traffic camera perspective, annotated by vehicle makes, models and years of construction for machine learning methods (ML) in the scope (task) of vehicle make and model recognition (VMMR).

The data set contains 162 vehicle models from 43 brands with 200 images each, as well as 8 sub-data sets each to be able to investigate different imaging qualities. In addition to the classification annotations, the data set also contains label images for semantic segmentation, as well as information on image and scene properties, as well as vehicle color.

The dataset was presented in May 2024 by Anne Sielemann, Stefan Wolf, Masoud Roschani, Jens Ziehn and Jürgen Beyerer in the publication: Sielemann, A., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2024). Synset Boulevard: A Synthetic Image Dataset for VMMR. In 2024 IEEE International Conference on Robotics and Automation (ICRA).

The model information is based on information from the ADAC online database (www.adac.de/rund-ums-fahrzeug/autokatalog/marken-modelle).

The data was generated using the simulation environment OCTANE (www.octane.org), which uses the Cycles ray tracer of the Blender project.

The dataset's website provides detailed information on the generation process and model assumptions. The dataset is therefore also intended to be used for the suitability analysis of simulated, synthetic datasets.

The data set was developed as part of the Fraunhofer PREPARE program in the "ML4Safety" project with the funding code PREPARE 40-02702, as well as funded by the "Invest BW" funding program of the Ministry of Economic Affairs, Labour and Tourism as part of the "FeinSyn" research project.

Comprehensive dataset of 6,256 Auto machine shops in United States as of June, 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.

Comprehensive dataset of 733 Auto machine shops in California, United States as of June, 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.

Auto Repair Software Market Outlook

The global auto repair software market size was valued at approximately USD 1.3 billion in 2023 and is projected to reach around USD 2.8 billion by 2032, growing at a CAGR of 8.5% during the forecast period. The significant growth factor driving this market is the increasing digitization within the automotive repair industry, alongside the growing need for efficient management of repair operations.

One of the primary growth drivers of the auto repair software market is the rising adoption of cloud-based solutions. These solutions offer numerous benefits, including scalability, cost efficiency, and ease of access, making them highly favorable among small and medium-sized enterprises (SMEs) as well as large enterprises. The ability to access data remotely and in real time is revolutionizing the way auto repair shops manage their operations, leading to improved customer satisfaction and reduced turnaround times. Furthermore, the continuous advancements in cloud technology and increasing internet penetration are anticipated to bolster market growth.

Another significant factor contributing to the market's growth is the increasing complexity of modern vehicles, which necessitates the use of sophisticated software to diagnose and repair issues effectively. Auto repair software solutions provide extensive databases, diagnostic tools, and integration capabilities that support technicians in handling these complexities. The rapid evolution of automotive technology, including the adoption of electric and autonomous vehicles, is further expected to drive the demand for advanced auto repair software solutions.

The growing emphasis on operational efficiency and profitability among automotive service providers is also propelling market expansion. Auto repair software assists in streamlining various processes, such as inventory management, billing and invoicing, service history tracking, and appointment scheduling. These features not only enhance the overall efficiency of repair shops but also contribute to reducing operational costs and increasing revenue generation. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) in these software solutions is anticipated to provide predictive maintenance insights, further enhancing their utility and market adoption.

In the realm of automotive repairs, Auto Collision Estimating Software has emerged as a pivotal tool for repair shops aiming to enhance their service delivery. This software provides detailed estimates for collision repairs, enabling technicians to assess damage accurately and efficiently. By integrating with existing auto repair software systems, it streamlines the estimation process, reducing the time taken to generate quotes and improving customer satisfaction. The precision offered by such software not only aids in maintaining transparency with clients but also helps in optimizing repair costs by providing accurate parts and labor estimates. As the complexity of vehicle designs increases, the reliance on sophisticated estimating tools becomes even more crucial for collision repair businesses.

Regionally, North America is expected to dominate the auto repair software market due to the high concentration of automotive service providers and the early adoption of advanced technologies in the region. The presence of key market players and the increasing demand for efficient repair management solutions are contributing to this dominance. Additionally, the Asia Pacific region is projected to witness significant growth during the forecast period, driven by the expanding automotive industry, increasing number of vehicles on the road, and rising awareness regarding digital solutions among repair shops.

Deployment Type Analysis

The deployment type segment of the auto repair software market is bifurcated into on-premises and cloud-based solutions. On-premises solutions involve the installation of software on local servers, providing users with complete control over their data and operations. This deployment model is preferred by larger enterprises with substantial IT infrastructure, capable of handling the associated maintenance and security requirements. Despite its benefits, the on-premises model faces challenges such as higher upfront costs, limited scalability, and the need for regular updates and maintenance.

<a href="https://dataintelo.com/report/global-auto-body-sof

Comprehensive dataset of 25 Auto machine shops in Montana, 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.

Comprehensive dataset of 59 Auto machine shops in New Mexico, United States as of June, 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.

Comprehensive dataset of 26 Auto machine shops in West Virginia, 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.

Comprehensive dataset of 10 Auto machine shops in Delaware, 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.

Embedded Database Management Systems Market size was valued at USD 10.8 Billion in 2024 and is projected to reach USD 18.70 Billion by 2031, growing at a CAGR of 7.1% during the forecasted period 2024 to 2031.

The Embedded Database Management Systems (DBMS) market is driven by the increasing demand for real-time data processing and management across various embedded systems, such as IoT devices, smartphones, automotive systems, and industrial equipment. The rise of connected devices and edge computing has amplified the need for lightweight, efficient, and scalable embedded databases that can operate within resource-constrained environments. Growing adoption of embedded systems in industries like healthcare, automotive, telecommunications, and consumer electronics is also boosting the demand for robust DBMS solutions. Additionally, advancements in AI, machine learning, and data analytics are driving the integration of more sophisticated embedded databases to enable real-time decision-making and enhance device performance.

Comprehensive dataset of 23 Auto machine shops in Maine, United States as of June, 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.

Comprehensive dataset of 66 Auto machine shops in Arkansas, 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.

Comprehensive dataset of 987 Auto machine shops in Hungary 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.

Comprehensive dataset of 299 Auto machine shops in Michigan, 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.

Comprehensive dataset of 8 Auto machine shops in Vermont, United States as of June, 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.

Comprehensive dataset of 35 Auto machine shops in Nebraska, United States as of June, 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.

Comprehensive dataset of 10 Auto machine shops in Wyoming, 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.

Comprehensive dataset of 29 Auto machine shops in New Hampshire, 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.

Comprehensive dataset of 651 Auto machine shops in Texas, 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.