Car Production in the United States increased to 11.04 Million Units in August from 10.42 Million Units in July of 2025. This dataset provides - United States Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides a comprehensive overview of motor vehicle production from 1950 to 2023, covering global trends. It includes annual production numbers for various years, highlighting peak production values and the year they occurred. The dataset is useful for analyzing long-term automotive industry trends, economic impacts, and production shifts over time. Potential Use Cases: Trend analysis in the automotive industry Economic impact assessment Forecasting vehicle production Understanding peak production years Feel free to ask me Any question and if you need any other dataset let me know i am practicing web scraping , i will do it and upload on kaggle

This dataset provides values for CAR PRODUCTION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides comprehensive information about used cars available for sale in the United States. It includes detailed data on various aspects of each vehicle, making it a valuable resource for car buyers, sellers, and data enthusiasts. The dataset contains the following key attributes:

• Brand: The brand or manufacturer of the car.
• Model: The specific model of the car.
• Mileage: The number of miles the car has been driven.
• Year: The manufacturing year of the car.
• Status: Indicates whether the car is new, used, or certified pre-owned.
• Dealer: Information about the dealer or seller offering the car.
• Price: The listed price of the car in USD.

This dataset is ideal for data analysis, machine learning projects, and market research related to the used car industry in the United States. Whether you are interested in predicting car prices, understanding market trends, or simply searching for your next vehicle, this dataset provides a wealth of information to explore.

Data Source: More info on my GitHub repository

Data Format: CSV

Car Production in Germany decreased to 353600 Units in October from 391740 Units in September of 2025. This dataset provides - Germany Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

Context

Automobile data holds immense importance as it offers insights into the functioning and efficiency of the automotive industry. It provides valuable information about car models, specifications, sales trends, consumer demographics, and preferences, which car manufacturers and dealerships can leverage to optimize their operations and enhance customer experiences. By analyzing data on vehicle reliability, fuel efficiency, safety ratings, and resale values, the automotive industry can identify trends and implement strategies to produce more reliable and environmentally friendly vehicles, improve safety standards, and enhance the overall value of cars for consumers. Moreover, regulatory bodies and policymakers rely on this data to enforce regulations, set emissions standards, and make informed decisions regarding automotive policies and environmental impacts. Researchers and analysts use car purchase data to study market trends, assess the environmental impact of various vehicle types, and develop strategies for sustainable growth within the industry. In essence, car purchase data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the automotive sector.

Content

This dataset comprises diverse parameters relating to car purchases and ownership on a global scale. The dataset prominently incorporates fields such as 'First Name', 'Last Name', 'Country', 'Car Brand', 'Car Model', 'Car Color', 'Year of Manufacture', and 'Credit Card Type'. These columns collectively provide comprehensive insights into customer demographics, vehicle details, and payment information. Researchers and industry experts can leverage this dataset to analyze trends in car purchasing behavior, optimize the customer car-buying experience, evaluate the popularity of car brands and models, and understand payment preferences within the automotive industry.

Dataset Glossary (Column-wise)

• First Name - The first name of the car purchaser.
• Last Name - The last name of the car purchaser.
• Country - The country of residence of the car purchaser.
• Car Brand - The brand or manufacturer of the purchased car.
• Car Model - The specific model of the purchased car.
• Car Color - The color of the purchased car.
• Year of Manufacture - The year the car was manufactured.
• Credit Card Type - The type of credit card used for the car purchase.

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable mock datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Freepik

Thumbnail by: Car icons created by Freepik - Flaticon

This dataset provides a comprehensive analysis of global vehicle production from 1999 to 2021, categorized by country and vehicle type, including Light Commercial Vehicles (LC), Passenger Cars (PC), Trucks, and Buses. It also includes manufacturer rankings based on production volume, sourced from the Organisation Internationale des Constructeurs d'Automobiles (OICA) or a similar automotive association.

What insights, trends, and industry shifts can you uncover in this production data? Explore the rise and fall of manufacturers, changing regional dominance, and the evolution of vehicle types over two decades. This dataset is ideal for market research, forecasting, and understanding how economic, technological, and policy changes have shaped global vehicle production. 🚗📊🔍

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.

The number of new and used vehicles and the sales dollars respectively sold by month.

MDOT MVA’s Customer Connect modernization project, implemented in July 2020, has increased the amount of data that is collected and used to calculate car sales. This data is updated in real time and may fluctuate based on external factors, including electronic submissions from dealers and other vendors.

Total Vehicle Sales in the United States decreased to 15.30 Million in October from 16.40 Million in September of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This datasets contains information about India's Category-Wise Automobiles Production for 2000 - 2016. Data from Ministry of Statistics and Programme Implementation, Government of India.Notes:* Vans are included in Passenger Cars

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.

Car Production in India decreased to 2801412 Units in October from 3073654 Units in September of 2025. This dataset provides - India Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

SpaceKnow uses satellite (SAR) data to capture activity in electric vehicles and automotive factories.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data that monitors the area which is covered with assembled light vehicles in square meters.

We offer 3 delivery options: CSV, API, and Insights Dashboard

Available companies Rivian (NASDAQ: RIVN) for employee parking, logistics, logistic centers, product distribution & product in the US. (See use-case write up on page 4) TESLA (NASDAQ: TSLA) indices for product, logistics & employee parking for Fremont, Nevada, Shanghai, Texas, Berlin, and Global level Lucid Motors (NASDAQ: LCID) for employee parking, logistics & product in US

Why get SpaceKnow's EV datasets?

Monitor the company’s business activity: Near-real-time insights into the business activities of Rivian allow users to better understand and anticipate the company’s performance.

Assess Risk: Use satellite activity data to assess the risks associated with investing in the company.

Types of Indices Available Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices. The first one is CFI-R which gives you level data, so it shows how many square meters are covered by metallic objects (for example assembled cars). The second one is CFI-S which gives you change data, so it shows you how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Product index This index monitors the area covered by manufactured cars. The larger the area covered by the assembled cars, the larger and faster the production of a particular facility. The index rises as production increases.

Product distribution index This index monitors the area covered by assembled cars that are ready for distribution. The index covers locations in the Rivian factory. The distribution is done via trucks and trains.

Employee parking index Like the previous index, this one indicates the area covered by cars, but those that belong to factory employees. This index is a good indicator of factory construction, closures, and capacity utilization. The index rises as more employees work in the factory.

Logistics index The index monitors the movement of materials supply trucks in particular car factories.

Logistics Centers index The index monitors the movement of supply trucks in warehouses.

Where the data comes from: SpaceKnow brings you information advantages by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the EV industry with just a 4-6 day lag, on average.

The EV data help you to estimate the performance of the EV sector and the business activity of the selected companies.

The backbone of SpaceKnow’s high-quality data is the locations from which data is extracted. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

Each individual location is precisely defined so that the resulting data does not contain noise such as surrounding traffic or changing vegetation with the season.

We use radar imagery and our own algorithms, so the final indices are not devalued by weather conditions such as rain or heavy clouds.

→ Reach out to get a free trial

Use Case - Rivian:

SpaceKnow uses the quarterly production and delivery data of Rivian as a benchmark. Rivian targeted to produce 25,000 cars in 2022. To achieve this target, the company had to increase production by 45% by producing 10,683 cars in Q4. However the production was 10,020 and the target was slightly missed by reaching total production of 24,337 cars for FY22.

SpaceKnow indices help us to observe the company’s operations, and we are able to monitor if the company is set to meet its forecasts or not. We deliver five different indices for Rivian, and these indices observe logistic centers, employee parking lot, logistics, product, and prod...

Car Production in Argentina increased to 47204 Units in October from 47108 Units in September of 2025. This dataset provides - Argentina Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Detailed Dataset Description: Car Sales Transactional Data This dataset provides a rich, multi-dimensional view of individual car sales transactions, capturing valuable information across customer demographics, car specifications, pricing metrics, payment details, sales performance, and seasonal or regional context. Each row in the dataset represents a single car sale transaction, allowing for granular-level analysis of how various factors influence profitability, sales trends, and customer behavior.

📅 Date & Temporal Context Date: The exact date of the transaction, allowing daily trend analysis.

Sale Year, Month, Quarter, Day of Week, and Season: These columns offer temporal segmentation that helps identify seasonal patterns, monthly performance, and weekday vs weekend trends.

🧑‍💼 Salesperson and Customer Information Salesperson: Identifies the individual responsible for the sale, useful for tracking salesperson performance, commission analysis, and productivity metrics.

Customer Name, Age, and Gender: Offers demographic insights, enabling segmentation by age group and gender, and understanding customer preferences in vehicle types and pricing.

🚗 Vehicle Details Car Make and Model: Specifies the brand and specific vehicle model sold.

Car Year: Indicates the model year of the vehicle, helpful in analyzing the popularity of newer vs older models.

💵 Financial and Sales Metrics Quantity: Number of cars sold in the transaction (typically 1, but can vary in business fleet cases).

Sale Price and Cost: Gross sale price and internal cost incurred by the dealership.

Profit: Computed as the difference between sale price and cost, giving direct insight into transaction-level profitability.

Discount: Shows the discount offered as a decimal (e.g., 0.05 = 5%), aiding in understanding the impact of promotions on sales.

💳 Payment & Incentive Structure Payment Method: Indicates how the customer paid (e.g., Cash, Loan, Credit), helping identify payment preferences over time or across customer types.

Commission Rate & Commission Earned: Details the salesperson's incentive structure and earnings from the sale, supporting analysis of commission efficiency, reward optimization, and employee motivation.

🌎 Geographic Coverage Sales Region: Highlights the physical region where the sale occurred (e.g., Alaska), allowing for regional performance comparison, mapping in BI tools, and assessing market penetration across different areas.

Use Cases and Applications This dataset can be effectively used for:

Business Intelligence Dashboards (e.g., Tableau, Power BI)

Sales & Profitability Analysis

Customer Demographics and Segmentation

Payment Method Trends

Salesperson Performance Monitoring

Seasonal Demand Forecasting

Regional Sales Comparisons

Commission Strategy Optimization

Its wide coverage across multiple dimensions makes it ideal for predictive modeling, trend visualization, and data storytelling in sales, marketing, and operations

Sports Car Dataset This dataset, scraped from Wikipedia, provides information on various sports car models, including their manufacturers, models, production years, styles, countries of origin, and additional notes. The data consists of 1,168 entries and covers a wide range of sports car models from different manufacturers across various time periods.

Key Attributes: Manufacturer: The company that produced the car. Model: The specific model name of the car. Years: The years of production or availability of the car model. Styles: The body style of the car (e.g., Roadster, Coupé). Country of Origin: The country where the car was originally manufactured. Notes: Additional information or unique features about the car (this column has many missing values).

This dataset provides detailed information about car sales in India for the year 2024. It includes monthly sales figures for various car makes and models, along with additional metrics and attributes that help analyze market trends and performance. The dataset is ideal for data analysis, visualization, and understanding the automobile market dynamics in India.

Features: - Make: The manufacturer of the car - Model: The specific car model - January Sales to December Sales: Monthly sales data for each car model in 2024. - Total Sales: The cumulative sales for the entire year. - Segment: The market segment the car belongs to - Body Type: The type of car body - Month-on-Month Percentage (MoM %): Percentage change in sales compared to the previous month. - Year-on-Year Percentage (YoY %): Percentage change in sales compared to the same month in 2023.

Potential Use Cases: - Analyzing car sales trends over 2024. - Comparing performance of car models and manufacturers. - Studying market preferences for segments and body types. - Identifying seasonal trends in car sales. - Developing sales forecasts or business strategies for the automobile industry.

This dataset was sourced from Auto Punditz (https://www.autopunditz.com/). The website allows sharing the data but does not specify whether modifications are permitted. As a precaution, this dataset is shared under the CC BY-ND 4.0 license, which prohibits modification. Please refer to the original website's terms of use for more information.