The car_price.csv file contains a dataset of various car-models.

The dataset contains 205 rows and 26 columns(features) of which 25 are independent features. Below shows a detailed information of feature names with its labels and datatypes.

It is a regression problem where with the various features we are expected to predict the price of a car.

The dataset doesn't contain any null values.

Independent features:

Features Labels Datatype

symboling 6 int64 fueltype 2 object aspiration. 2 object doornumber. 2 object carbody 5 object drivewheel 3 object enginelocation 2 object wheelbase 53 float64 carlength 75 float64 carwidth 44 float64 carheight 49 float64 curbweight 171 int64 enginetype 7 object cylindernumber 7 object enginesize 44 int64 fuelsystem 8 object boreratio 38 float64 stroke 37 float64 compressionratio 32 float64 horsepower 59 int64 peakrpm 23 int64 citympg 29 int64 highwaympg 30 int64

**Target/Dependent variable: ** For the dataset we have price as our dependent feature with its datatype float64, hence using Regression Models we are expected to predict the value of price

Features Labels Datatype

price 189 float64

Gain valuable insights into the automotive market with our comprehensive Car Prices Dataset. Designed for businesses, analysts, and researchers, this dataset provides real-time and historical car pricing data to support market analysis, pricing strategies, and trend forecasting.

Dataset Features

Vehicle Listings: Access detailed car listings, including make, model, year, trim, and specifications. Ideal for tracking market trends and pricing fluctuations. Pricing Data: Get real-time and historical car prices from multiple sources, including dealerships, marketplaces, and private sellers. Market Trends & Valuations: Analyze price changes over time, compare vehicle depreciation rates, and identify emerging pricing trends. Dealer & Seller Information: Extract seller details, including dealership names, locations, and contact information for lead generation and competitive analysis.

Customizable Subsets for Specific Needs Our Car Prices Dataset is fully customizable, allowing you to filter data based on vehicle type, location, price range, and other key attributes. Whether you need a broad dataset for market research or a focused subset for competitive analysis, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Pricing Strategy: Track vehicle price trends, compare competitor pricing, and optimize pricing strategies for dealerships and resellers. Automotive Valuation & Depreciation Studies: Analyze historical pricing data to assess vehicle depreciation rates and predict future values. Competitive Intelligence: Monitor competitor pricing, dealership inventory, and promotional offers to stay ahead in the market. Lead Generation & Sales Optimization: Identify potential buyers and sellers, track demand for specific vehicle models, and enhance sales strategies. AI & Predictive Analytics: Leverage structured car pricing data for AI-driven forecasting, automated pricing models, and trend prediction.

Whether you're tracking car prices, analyzing market trends, or optimizing sales strategies, our Car Prices Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

1) Data Introduction • The Extended Dataset for Used Car Prices Regression Dataset is a structured dataset designed to predict the collector’s value of used vehicles based on a variety of features related to car pricing. It includes key attributes such as model year, brand, model, vehicle type, fuel efficiency (MPG), and MSRP. The collection_car variable indicates whether a car is considered a collector’s item due to its rarity or historical significance.

2) Data Utilization (1) Characteristics of the Extended Dataset for Used Car Prices Regression Dataset: • The dataset contains key factors influencing vehicle value, such as miles_per_gallon, premium_version, and msrp (Manufacturer's Suggested Retail Price).

(2) Applications of the Extended Dataset for Used Car Prices Regression Dataset: • Collector car prediction model development: The dataset can be used to train machine learning classification models that predict whether a vehicle has collector value based on its characteristics. • Rare vehicle market analysis and targeted marketing: By identifying vehicles with high collector value, the dataset supports applications in used car marketing, insurance planning, and premium vehicle recommendation systems.

Dataset

This dataset was created by Saumya

Released under CC0: Public Domain

Contents

It contains the following files:

jvbf2e/used-car-price-prediction dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Lukas Exl

Contents

Analysis of ‘Car Prices Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sidharth178/car-prices-dataset on 29 August 2021.

--- Dataset description provided by original source is as follows ---

Context

With the rise in the variety of cars with differentiated capabilities and features such as model, production year, category, brand, fuel type, engine volume, mileage, cylinders, colour, airbags and many more, we are bringing a car price prediction challenge for all. We all aspire to own a car within budget with the best features available. To solve the price problem we have created a dataset of 19237 for the training dataset and 8245 for the test dataset.

Data Description

Train.csv - 19237 rows x 18 columns (Includes Price Columns as Target) - Attributes - ID - Price: price of the care(Target Column) - Levy - Manufacturer - Model - Prod. year - Category - Leather interior - Fuel type - Engine volume - Mileage - Cylinders - Gear box type - Drive wheels - Doors - Wheel - Color - Airbags Test.csv - 8245 rows x 17 columns

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by KangChul45

Contents

By 2028, a passenger car in Finland is projected to cost on average ****** euros, a stable average compared to the 2027 forecast. Car prices are expected to increase from 2024, after a slight year-over-year decrease compared to 2023.

Problem Statement

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:

Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large data set of different types of cars across the America market.

Business Goal

We are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.

Please Note : The dataset provided is for learning purpose. Please don’t draw any inference with real world scenario.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Rebel Cars on 2025-09-12. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Dataset

This dataset was created by Akash Kumar

Contents

The Canadian passenger car market rose slightly to $34.1B in 2024, increasing by 2.2% against the previous year. The market value increased at an average annual rate of +2.3% from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded throughout the analyzed period. Passenger car consumption peaked in 2024 and is expected to retain growth in years to come.

Snapshot img

Used Car Market Size 2025-2029

The used car market size is forecast to increase by USD 885.3 billion, at a CAGR of 7.4% between 2024 and 2029.

The market is experiencing dynamic shifts, driven by intensifying competition leading to an escalating launch of new car models and increasing consumer preferences for alternative mobility solutions. These trends are reshaping the market landscape, presenting both opportunities and challenges for stakeholders. Competition in the market is escalating, prompting automakers to introduce new models at a faster pace to maintain market share. This trend, in turn, is increasing the availability of pre-owned vehicles, providing consumers with a wider range of options. Meanwhile, consumer preferences are evolving, with a growing demand for car subscription services and car-sharing solutions.
These services cater to consumers seeking flexible, cost-effective mobility solutions, particularly in urban areas. However, this shift towards alternative mobility models poses a challenge for traditional used car dealers, requiring them to adapt and innovate to remain competitive. Digital marketing, including social media, mobile apps, and data analytics, helps sellers reach a wider audience. The market is undergoing significant transformation, fueled by increasing competition and evolving consumer preferences. Companies seeking to capitalize on opportunities and navigate challenges effectively must stay abreast of these trends and adapt their strategies accordingly. This may involve exploring new business models, such as car subscription services, or enhancing their offerings to cater to the changing needs of consumers.

What will be the Size of the Used Car Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Internal combustion engines power the majority of the market, but the emergence of electric vehicles is reshaping the landscape. Steering systems and suspension systems ensure optimal vehicle handling, while safety features such as backup cameras, parking sensors, and blind spot monitoring are becoming increasingly essential. Title transfer and engine displacement are crucial components of the sales process, with customer service and fuel efficiency key differentiators for dealers. Inventory management and pricing strategies are critical for wholesale auctions and online auto dealers, who must navigate the complex interplay of supply and demand. Vehicle registration and title transfer processes can be streamlined through digital means, and car refurbishment and connected car technology enhance safety and convenience.

Car loans and auto auctions offer financing options for buyers, while certified pre-owned vehicles and vehicle history reports provide transparency and value assurance. Adaptive cruise control and lane departure warning systems are among the advanced technologies enhancing the driving experience. Fuel efficiency and body panels are essential considerations for buyers, with infotainment systems and navigation systems adding convenience and value. The market's continuous evolution underscores the importance of staying informed and adaptable to changing consumer preferences and industry trends.

How is this Used Car Industry segmented?

The used car industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Vehicle Type

  Compact
  SUV
  Mid size


Channel

  Organized
  Unorganized


Fuel Type

  Diesel
  Petrol


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Vehicle Type Insights

The Compact segment is estimated to witness significant growth during the forecast period. The compact car segment in the used automobile market experiences significant growth due to increasing consumer preference for personal mobility and the availability of advanced features in compact vehicles. APAC and Europe lead the market, contributing a substantial share to the compact segment. Compact cars, which sit between subcompact and mid-size vehicles, offer easier handling in traffic congestion and lower emissions. Popular pre-owned compact models include the Fiat Panda and Volkswagen Golf in Europe. Inventory management plays a crucial role in the market, ensuring a steady supply of various models. Used car dealers source vehicles from private sellers, wholesale auctions, and trade-ins.

Vehicle history reports help assess the con

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nvidia: The Future of Gaming, AI, and Self-Driving Cars

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

AI-powered price forecasts for CAR.DE stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Luxury vehicles and executive cars, due to their premium nature, are Volkswagen's segments with the highest average price throughout the forecast period. Medium cars, one of the brand's most popular segments, are projected to have an average price of ****** euros in 2028.

Dataset

This dataset was created by Pranya Chandratre

Released under MIT

Contents

The Romanian passenger car market skyrocketed to $7.3B in 2024, growing by 16% against the previous year. The market value increased at an average annual rate of +3.0% over the period from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded in certain years. Passenger car consumption peaked in 2024 and is expected to retain growth in the immediate term.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

CAR CARSALES.COM LIMITED.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data