The Car Dataset is a comprehensive collection of data related to the sale cars. This dataset contains various attributes that provide valuable insights into the pricing, fuel efficiency, ownership history, and other significant features of the vehicles.

Dataset Description: The dataset consists of 9 columns, each representing different characteristics of the cars. The columns include "Car_Name" "Year" "Selling_Price" "Present_Price" "Kms_Driven" "Fuel_Type" "Seller_Type" "Transmission" and "Owner."

Key Features: Car Name: The "Car_Name" column provides the names of the different car models available in the dataset, helping us identify and study the various car brands and models.

Year of Manufacture: The "Year" column allows us to analyze the age of the cars, which is an important factor in determining their selling price.

Present Price: The "Present_Price" column indicates the current market price of the cars, which can have a significant impact on their selling price.

Kilometers Driven: The "Kms_Driven" column provides information about the total distance covered by each car, which is another critical factor affecting the selling price.

Fuel Type: The "Fuel_Type" column categorizes the cars based on the type of fuel they use, such as petrol, diesel, or CNG.

Seller Type: The "Seller_Type" column distinguishes between individual sellers and dealerships, which may influence the pricing strategy.

Transmission Type: The "Transmission" column classifies the cars as manual or automatic, which can also affect the selling price.

Owner History: The "Owner" column represents the number of previous owners for each vehicle, which may be a factor considered by potential buyers.

Selling Price: The "Selling_Price" column is the target variable, representing the actual selling price of the used cars. This is the value we aim to predict using the other features. Overall, this dataset provides an excellent opportunity for exploratory data analysis and building predictive models to forecast the selling price of used cars. By applying machine learning algorithms and regression techniques, we can uncover valuable insights into the used car market, understand the impact of different features on pricing, and make well-informed decisions related to buying and selling pre-owned vehicles.

This dataset captures detailed specifications of 10,000 cars manufactured between 2010 and 2020. It provides a comprehensive resource for various analytical and machine learning projects, including price prediction, market trend analysis, and comparative studies among different car makes and models.

The dataset includes essential attributes such as the make and model of the car, the year of manufacture, engine size, fuel type, and the price in USD. These attributes allow for in-depth analysis and modeling, facilitating insights into market trends, price prediction, and comparative studies among different car makes and models.

The car makes and models were selected from popular vehicles available in the market, reflecting consumer preferences and market availability. The prices, engine sizes, and fuel types were randomly assigned within realistic ranges, ensuring that the dataset remains representative and suitable for various analytical purposes.

Whether you are an automotive enthusiast, a data scientist, or a machine learning practitioner, this dataset offers a rich foundation for your projects. It can be used to develop machine learning models to predict car prices, analyze trends in the automotive market, compare different car makes and models, study the distribution and evolution of fuel types, or for educational purposes in data science and machine learning courses.

The data was meticulously generated using the Faker library to simulate realistic car specifications. This dataset is versatile and can be used for a wide array of projects, making it a valuable tool in your analytical and predictive endeavors.

The dataset is scraped from Bikroy.com and contains information pertaining to second hand cars that are used in Bangladesh. The dataset contains 1209 records. The columns in the dataset are as follows:

1. car_name
2. brand
3. car_model
4. model_year
5. transmission
6. body_type
7. fuel_type
8. engine_capacity
9. kilometers_run
   10.price

This dataset contains raw data on car sales from Cars.com. It includes detailed information about each car, such as the model, condition, mileage, year of production, dealer name, as well as total and monthly prices. The data was extracted from 500 pages of the site and is stored in its raw format, ready for cleaning and further analysis. This dataset is intended for research and analysis purposes in the automotive industry, enabling market studies, price forecasting, and trend analysis.

Autos include all passenger cars, including station wagons. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed by Eurostat in cooperation between the United Nations Economic Commission for Europe (UNECE) and the International Transport Forum (ITF) at OECD.

The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; its completeness varies from country to country.

Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITF and the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide.

The Common Questionnaire collects aggregated annual data on:

• Railway transport
• Road transport
• Inland waterways transport
• Oil pipelines transport
• Road traffic - vehicle-kilometres
• Buses (and coaches)

For each mode of transport, the Common Questionnaire covers some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport):

• Infrastructure (All modes)
• Transport equipment (Rail, Road and Inland Waterways)
• Enterprises, economic performance and employment (All modes)
• Traffic (Rail, Road and Inland Waterways)
• Transport measurement (All modes).

As its name suggests, the theme "Road traffic" focuses on "traffic" only, on road:

• of national vehicle stocks, on national and foreign territories, by type of road; and by vehicle category, fuel type and age. These data should rely on odometer readings;
• of national- and foreign-registered vehicles on national territories. These data should be issued from road censuses.

The theme “Buses and coaches” covers detailed information on road “traffic” (vkm) and “transport measurement” (passengers, passenger-km) performed by buses and coaches.

The data collection on Common Questionnaire was streamlined twice in the recent years:

• In 2013, among other changes, the collection of employment data was terminated;
• In 2019, among other changes, the collection of gas pipeline data was terminated, and the number of “Buses” indicators was reduced.

The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.

Description:

This dataset offers a comprehensive overview of the automotive market, capturing a wide range of information on cars from various brands, models, and types. It includes critical details such as pricing, mileage, ratings, and reviews, making it an invaluable resource for data analysis and machine learning projects in the automotive domain.

COLUMNS DESCRIPTION

1. CAR ID:

   • A unique identifier assigned to each car in the dataset. This ensures that each entry is distinct and can be referenced individually.

2. PARENT COMPANY:

   • The name of the parent company that owns the car brand. This helps in analyzing the distribution of vehicles across different automotive conglomerates.

3. MAKE ORIGIN:

   • The country or region where the car's brand originates. This is useful for geographical analysis and understanding global market presence.

4. BRAND:

   • The specific brand or manufacturer of the car. This column is essential for brand-specific analysis, such as brand popularity and market share.

5. MODEL/CLASS:

   • The model or class designation of the car, often indicating the specific type or series within a brand's lineup. This helps differentiate between various models produced by the same brand.

6. CAR NAME:

   • The full name of the car, often combining the brand, model, and sometimes additional identifiers like trim levels. This column provides a detailed reference for each vehicle.

7. MODEL (YEAR):

   • The year the car model was released. This is important for understanding the age of the car and for time-series analysis of model releases.

8. CAR TYPE:

   • The classification of the car, such as sedan, SUV, truck, etc. This column helps in analyzing the distribution of different car types and their market trends.

9. IMAGE:

   • A link or path to an image of the car provided by dealer.

10. PRICE RANGE:

    • The general price category of the car (e.g., low, luxury). This provides a broad overview of the market segment in which the car falls.

11. PRICE($):

    • The exact price of the car in US dollars. This column is crucial for price analysis, comparisons, and building predictive models for pricing.

12. AGE OF CAR:

    • The age of the car, calculated from the model year to the present. This helps in analyzing depreciation, resale value, and market demand for different age groups.

13. STOCK TYPE:

    • Indicates whether the car is new, used, or certified. This information is essential for analyzing market segments and understanding the condition of vehicles in the dataset.

14. MILEAGE:

    • The total distance the car has traveled, typically measured in miles. This is a key factor in determining the car’s condition and market value.

15. RATING:

    • The average user rating for the car, usually on a scale (e.g., 1 to 5). This provides insight into customer satisfaction and perceived quality.

16. REVIEW:

    • The number of reviews the car has received.

17. DEALER NAME:

    • The name of the dealership selling the car. This is useful for analyzing dealer performance and market presence.

18. DEALER LOCATION (CITY):

    • The city where the dealership is located. This allows for geographical analysis at the city level.

19. DEALER LOCATION (STATE):

    • The state where the dealership is located. This allows for geographical analysis at the state level and can help identify regional trends.

Use Cases:

• Market Analysis: Evaluate pricing trends and brand popularity across different regions.
• Customer Sentiment Analysis: Use ratings and reviews to gauge customer satisfaction and identify key factors that influence buying decisions.
• Predictive Modeling: Build models to predict car prices based on various factors such as brand, model, mileage, and more.
• Dealer Performance: Analyze dealer distribution and performance across different locations.

This dataset is ideal for data scientists, analysts, and automotive enthusiasts looking to explore and analyze various aspects of the car market.

In the fourth quarter of 2024, there were around ***** million vehicles operating on roads throughout the United States. Almost **** million used vehicles changed owners in the U.S. between the fourth quarter of 2023 and the fourth quarter of 2024, while new registrations of vehicles came to about **** million units during that period. Automotive market disparities The number of licensed drivers had been steadily increasing up to just under ******* in 2023, but the automotive market has been impacted by economic developments over the past few years. The U.S. vehicle fleet is aging, reflected by the slow increase in the average vehicle age from **** years in 2018 to over ** years in 2024. This is in part due to market disparities. The average selling price of new vehicles has been increasing to nearly ****** U.S. dollars in 2024, up from under ****** in 2016. Used car prices have been declining after the chip shortages linked to the COVID-19 pandemic, reaching around ****** U.S. dollars in 2024. The majority of U.S. car owners earned more than ****** U.S. dollars per years, with the ****** to ****** income group owning over ** percent of the vehicles in use. The boom of the used vehicle market Close to ************* of new car buyers were born between 1946 and 1981, with Gen X being the leading consumers by age group for both the new and used vehicle market. Used light vehicle sales have been steadily increasing since 2010, representing well over double the size of the new light vehicle market in 2024. With a product range priced below new vehicle prices, used vehicles are gaining momentum in the United States. The average American household spends some ***** U.S. dollars on vehicle purchases annually, with consumers in income groups earning above 100,000 U.S. dollars per year spending above ***** dollars annually on car buying. Used vehicle financing options are naturally more affordable than new vehicle financing options, with an average monthly payment over *** dollars for loan payments for new vehicles.

The Car Sales Dataset is a collection of data that contains information on various car models sold in the market. The dataset includes variables such as the price of the car in thousands of dollars, engine size, horsepower, fuel efficiency, and sales. 1. Price_in_thousands: This variable represents the price of the car in thousands of dollars, which is a measure of the car's cost. 2. Engine_size: This variable represents the size of the engine in cubic centimeters, which is a measure of the car's power. 3. Horsepower: This variable represents the power of the car's engine in horsepower, which is a measure of the car's ability to accelerate and maintain speed. 4. Fuel_efficiency: This variable represents the number of miles per gallon (mpg) that the car can travel on a single gallon of fuel, which is a measure of the car's fuel efficiency. 5. Sales: This variable represents the number of units of the car sold in a given period, which is a measure of the car's popularity and demand in the market.

Overall, this dataset can be used to analyze the relationships between the different variables and to predict the sales of a car based on its price, engine size, horsepower, and fuel efficiency. It can be helpful for businesses and consumers alike in making informed decisions about buying and selling cars.

Passenger Cars: Per One Million Units of Current USD GDP data was reported at 15.706 Ratio in 2022. This records a decrease from the previous number of 15.837 Ratio for 2021. Passenger Cars: Per One Million Units of Current USD GDP data is updated yearly, averaging 20.026 Ratio from Dec 1994 (Median) to 2022, with 28 observations. The data reached an all-time high of 27.953 Ratio in 2003 and a record low of 14.992 Ratio in 2015. Passenger Cars: Per One Million Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Costa Rica – Table CR.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. [COVERAGE] PASSENGER CARS A passenger car is a road motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). It refers to category M1 of the UN Consolidated Resolution on the Construction of Vehicles. Passenger cars, vans designed and used primarily for transport of passengers, taxis, hire cars, ambulances and motor homes are not included. Light goods road vehicles, motor-coaches and buses and mini-buses/mini-coaches are not included. Microcars (needing no permit to be driven), taxis and passenger hire cars, provided that they have fewer than ten seats, are included. [STAT_CONC_DEF] PASSENGER CARS The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.

The Self Driving Cars dataset comprises images and associated labeled semantic segmentations obtained using the CARLA self-driving car simulator. This dataset was created within the context of the Lyft Udacity Challenge. It serves as valuable data for training machine learning algorithms to recognize semantic segmentation of objects like cars, roads, and more.

IL: First Registrations of Brand New Road Vehicles: Per One Million Units of Current USD GDP data was reported at 0.582 Ratio in 2020. This records a decrease from the previous number of 0.691 Ratio for 2019. IL: First Registrations of Brand New Road Vehicles: Per One Million Units of Current USD GDP data is updated yearly, averaging 0.823 Ratio from Dec 2011 (Median) to 2020, with 10 observations. The data reached an all-time high of 0.941 Ratio in 2016 and a record low of 0.582 Ratio in 2020. IL: First Registrations of Brand New Road Vehicles: Per One Million Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Israel – Table IL.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. FIRST REGISTRATIONS A passenger car is a road motor vehicle, other than a motorcycle, intended for the carriage of passengers and designed to seat no more than nine persons (including the driver). The term 'passenger car' therefore covers microcars (need no permit to be driven), taxis and hired passenger cars, provided that they have fewer than ten seats. This category may also include pick-ups. A good road motor vehicle is any single road motor vehicle designed to carry goods (lorry), or any coupled combination of road vehicles designed to carry goods, (i.e. lorry with trailer(s), or road tractor with semi-trailer and with or without trailer).

DE: First Registrations of Brand New Passenger Cars: Per One Million Units of Current USD GDP data was reported at 0.767 Ratio in 2020. This records a decrease from the previous number of 0.934 Ratio for 2019. DE: First Registrations of Brand New Passenger Cars: Per One Million Units of Current USD GDP data is updated yearly, averaging 1.116 Ratio from Dec 1994 (Median) to 2020, with 27 observations. The data reached an all-time high of 1.734 Ratio in 2000 and a record low of 0.767 Ratio in 2020. DE: First Registrations of Brand New Passenger Cars: Per One Million Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. FIRST REGISTRATIONS A passenger car is a road motor vehicle, other than a motorcycle, intended for the carriage of passengers and designed to seat no more than nine persons (including the driver). The term 'passenger car' therefore covers microcars (need no permit to be driven), taxis and hired passenger cars, provided that they have fewer than ten seats. This category may also include pick-ups.

EE: Passenger Cars: Per One Million Units of Current USD GDP data was reported at 25.259 Ratio in 2019. This records an increase from the previous number of 24.369 Ratio for 2018. EE: Passenger Cars: Per One Million Units of Current USD GDP data is updated yearly, averaging 29.343 Ratio from Dec 1995 (Median) to 2019, with 25 observations. The data reached an all-time high of 98.038 Ratio in 1995 and a record low of 22.668 Ratio in 2008. EE: Passenger Cars: Per One Million Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Estonia – Table EE.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. PASSENGER CARS The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.; PASSENGER CARS A passenger car is a road motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). It refers to category M1 of the UN Consolidated Resolution on the Construction of Vehicles. Passenger cars, vans designed and used primarily for transport of passengers, taxis, hire cars, ambulances and motor homes are not included. Light goods road vehicles, motor-coaches and buses and mini-buses/mini-coaches are not included. Microcars (needing no permit to be driven), taxis and passenger hire cars, provided that they have fewer than ten seats, are included.

Passenger Cars: Per One Thousand Inhabitants data was reported at 581.172 Ratio in 2023. This records a decrease from the previous number of 581.912 Ratio for 2022. Passenger Cars: Per One Thousand Inhabitants data is updated yearly, averaging 542.564 Ratio from Dec 1994 (Median) to 2023, with 30 observations. The data reached an all-time high of 583.452 Ratio in 2021 and a record low of 488.283 Ratio in 1994. Passenger Cars: Per One Thousand Inhabitants data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. [COVERAGE] PASSENGER CARS A passenger car is a road motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). It refers to category M1 of the UN Consolidated Resolution on the Construction of Vehicles. Passenger cars, vans designed and used primarily for transport of passengers, taxis, hire cars, ambulances and motor homes are not included. Light goods road vehicles, motor-coaches and buses and mini-buses/mini-coaches are not included. Microcars (needing no permit to be driven), taxis and passenger hire cars, provided that they have fewer than ten seats, are included. [COVERAGE] PASSENGER CARS In 2007, vehicles temporarily taken off the road have been removed, that creates a break in the series. [STAT_CONC_DEF] PASSENGER CARS The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.

This car statistics dataset offers insights into the vehicular landscape of Finland, allowing businesses to tailor their strategies based on the types of vehicles prevalent in specific regions and the fuel preferences of diverse demographics.

Worldwide car sales grew to around ** million automobiles in 2024, up from around **** million units in 2023. Throughout 2020 and 2021, the sector experienced a downward trend on the back of a slowing global economy, while COVID-19 and the Russian war on Ukraine contributed to shortages in the automotive semiconductor industry and further supply chain disruptions in 2022. Despite these challenges, 2023 and 2024 sales surpassed pre-pandemic levels and are forecast to keep rising through 2025 and 2026. Covid-19 hits car demand It had been estimated pre-pandemic that international car sales were on track to reach ** million. While 2023 sales are still far away from that goal, this was the first year were car sales exceeded pre-pandemic values. The automotive market faced various challenges in 2023, including supply shortages, automotive layoffs, and strikes in North America. However, despite these hurdles, the North American market was among the fastest-growing regions in 2024, along with Eastern Europe and Asia, as auto sales in these regions increased year-on-year. Chinese market recovers After years of double-digit growth, China's economy began to lose steam in 2022, and recovery has been slow through 2023. China was the largest automobile market based on sales with around **** million units in 2023. However, monthly car sales in China were in free-fall in April 2022 partly due to shortages, fears over a looming recession, and the country grappling with the COVID-19 pandemic. By June of that same year, monthly sales in China were closer to those recorded in 2021.

This dataset contains information about vehicles (or units as they are identified in crash reports) involved in a traffic crash. This dataset should be used in conjunction with the traffic Crash and People dataset available in the portal. “Vehicle” information includes motor vehicle and non-motor vehicle modes of transportation, such as bicycles and pedestrians. Each mode of transportation involved in a crash is a “unit” and get one entry here. Each vehicle, each pedestrian, each motorcyclist, and each bicyclist is considered an independent unit that can have a trajectory separate from the other units. However, people inside a vehicle including the driver do not have a trajectory separate from the vehicle in which they are travelling and hence only the vehicle they are travelling in get any entry here. This type of identification of “units” is needed to determine how each movement affected the crash. Data for occupants who do not make up an independent unit, typically drivers and passengers, are available in the People table. Many of the fields are coded to denote the type and location of damage on the vehicle. Vehicle information can be linked back to Crash data using the “CRASH_RECORD_ID” field. Since this dataset is a combination of vehicles, pedestrians, and pedal cyclists not all columns are applicable to each record. Look at the Unit Type field to determine what additional data may be available for that record. The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. The crash data published on the Chicago data portal mostly follows the data elements in SR1050 form. The current version of the SR1050 instructions manual with detailed information on each data elements is available here. Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.