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.

Dataset

This dataset was created by Saumya

Released under CC0: Public Domain

Contents

It contains the following files:

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.

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

Dataset

This dataset was created by Damilola Ogunsakin

Contents

Dataset

This dataset was created by Lukas Exl

Contents

The Used Car Market size was valued at USD 1.76 trillion in 2023 and is projected to reach USD 2.66 trillion by 2032, exhibiting a CAGR of 6.1 % during the forecasts period. The Used Car Market entails the business of buying and selling second-hand cars which offer affordable options compared to brand-new cars. Some of the applications of the used car market include; offering cheap means of transport for individuals and firms during hard economic times or where specific models of cars are not being produced as new. Applications cover the zones of individual and business usage, adjusting to private cars, business fleets, and rental cars. Today’s trends in the market are the growing availability of online sources for purchasing used cars, the constantly growing popularity of certified pre-owned automobiles with warranties and inspection programs, and the increased demand for electric and hybrid used cars due to the advancing environmental consciousness and fairly affordable prices. However, with the change in customer preference towards value and sustainability, the upgraded category of the used car market experiences consistent growth with the factors of accessibility and availability of various models and brands across the world. Recent developments include: In June 2023, Jardine Cycle & Carriage Limited, the investment arm of Hong Kong-based Jardine Matheson collaborated with Southeast Asian car marketplace Carro. According to a joint statement by the companies, this collaboration will provide Carro with access to a broader selection of high-quality used cars while enabling Carro to improve Republic Auto's digital services and offerings. The alliance aims to strengthen both companies' positions in the market and enhance their customer experiences. , In August 2022, Lexus, a subsidiary of TOYOTA MOTOR CORPORATION, launched the Lexus Certified Programme in the Indian market. This strategic move by Lexus is geared towards providing current Lexus vehicle owners the avenue to secure an elevated resale valuation for their automobiles. Moreover, the initiative aims to augment the accessibility and affordability of Lexus models for prospective customers. By implementing this certified program, Lexus aims to amplify customer contentment while bolstering the brand's prominence within the Indian automotive sector. .

1) Data Introduction • The USA - 2025 - Car Price Dataset includes 13 key characteristics, including vehicle price, brand, model, year, mileage, color, and title status, based on approximately 2,500 used car transaction information in the U.S., allowing you to analyze price trends and brand-specific preferences in the 2025 U.S. used car market.

2) Data Utilization (1) USA - 2025 - Car Price Dataset has characteristics that: • This dataset includes a variety of vehicle and transaction-related characteristics, including vehicle price, brand, model, year, mileage, title status, color, vehicle identification information (vin, lot). (2) USA - 2025 - Car Price Dataset can be used to: • Used Car Price Forecasting Model Development: Using key characteristics such as brand, model, year, mileage, etc., used car price forecasting and valuation models based on machine learning can be built. • Market trends and consumer analysis: Data analysis can be used to understand market trends and consumer preferences, including yearly and brand price changes, popular models, and correlations between mileage and price.

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

1) Data Introduction • The Used Car Prices in india dataset contains information on the prices and related characteristics of used cars sold in India. • Key features include the car’s brand, model, manufacturing year, mileage, fuel type, transmission type, number of owners, engine displacement, horsepower, number of seats, and new car price.

2) Data Utilization (1) Characteristics of the Used Car Prices in india Dataset • This dataset includes various attributes related to the prices of used cars traded in India, making it useful for analyzing trends in the used car market. • It provides valuable data for analyzing and predicting the factors that affect used car prices.

(2) Applications of the Used Car Prices in india Dataset • Market Analysis: This dataset is useful for analyzing trends in the used car market, understanding price fluctuations, and identifying patterns in market demand. • Predictive Modeling: Based on the various attributes, predictive models can be developed to estimate the prices of used cars. • Business Insights: This data can be leveraged to analyze consumer preferences, market demand, and pricing strategies, helping businesses formulate their market strategies.

Dataset

This dataset was created by RITIK MAHESHWARI

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.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Rebel Cars on 2025-09-18. 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 Abdelrahman Kamel

Released under Apache 2.0

Contents

A dataset of car running costs including depreciation, fuel, and SMR by car type, make, fuel, CO2, and price bands.

Unlock actionable insights to refine your product development and pricing strategies. Our comprehensive Car Data provides the data-driven edge you need to stay ahead of the curve in the competitive LCV market.

Essential for: - Product Development - Pricing Strategy - Market Analysis - Competitor Benchmarking - Total Cost of Ownership (TCO)

Discover hidden insights into historical pricing trends, market fluctuations, and seasonal patterns. Our unmatched Car Price Data empowers you to predict future pricing, eliminate guesswork, and make data-driven decisions. Benchmark against industry leaders to optimize profit margins and gain a competitive advantage.

Covering: France, UK, Italy, Poland, Netherlands, Spain, Belgium, Germany, Austria, Czechia, Portugal, Romania, Switzerland, Bulgaria, Croatia, Denmark, Hungary, Norway, Slovenia, Sweden, Ireland, Turkey, Morocco, Brazil, Argentina, Colombia, Mexico, Australia.

Unlock the potential of your LCV business with our comprehensive data. Make informed decisions, optimize pricing strategies, and drive growth.

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.

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