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Automobile Features and Price Analysis
Data Science and Analytics
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About
Information about cars is available, containing various characteristics of over 400 vehicles. This data covers specifications such as horsepower, engine size, number of cylinders, and drivetrain type. It includes pricing details like the manufacturer's suggested retail price (MSRP) and dealer cost, making it suitable for price prediction tasks and machine learning applications, such as linear regression models. The data is structured with distinct vehicle attributes, offering a solid basis for analysis in the automotive domain.
Columns
- name: The vehicle's name and model.
- sports_car: A boolean value indicating if the vehicle is a sports car.
- suv: A boolean value indicating if the vehicle is an SUV.
- wagon: A boolean value indicating if the vehicle is a wagon.
- minivan: A boolean value indicating if the vehicle is a minivan.
- pickup: A boolean value indicating if the vehicle is a pickup truck.
- all_wheel: A boolean value indicating if the vehicle has an all-wheel-drive (4x4) system.
- rear_wheel: A boolean value indicating if the vehicle has a rear-wheel-drive (RWD) system. If both
all_wheelandrear_wheelare false, the vehicle is front-wheel-drive (FWD). - msrp: The real cost or manufacturer's suggested retail price.
- dealer_cost: The cost of the vehicle to the dealer.
- eng_size: The size of the engine in litres.
- ncyl: The number of engine cylinders.
- horsepwr: The vehicle's horsepower (HP).
- city_mpg: The fuel consumption in miles per gallon (MPG) for city driving.
- hwy_mpg: The fuel consumption in miles per gallon (MPG) for highway driving.
- weight: The weight of the vehicle.
- wheel_base: The wheelbase measurement of the vehicle.
- length: The length of the vehicle.
- width: The width of the vehicle.
Distribution
The data is provided in a single CSV file named
Cars.csv with a size of 47.45 kB. It is structured into 428 records (rows) and 19 columns, with no missing values reported across the dataset.Usage
This data is ideal for building machine learning models, particularly for vehicle price prediction using linear regression. It can also be used for market analysis, feature extraction to understand which characteristics most influence price, and for creating categorical classifications of automobiles.
Coverage
The dataset focuses on automobile characteristics without specific geographical, temporal, or demographic limitations mentioned in the source material. It includes a variety of car types such as sports cars, SUVs, wagons, minivans, and pickups.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Engineers: Can use this data to build and test predictive models for vehicle pricing.
- Automotive Market Analysts: Can analyse trends in vehicle features and pricing structures.
- Students and Educators: Can use this as a practical example for teaching linear regression and data analysis concepts.
Dataset Name Suggestions
- Automobile Features and Price Analysis
- Vehicle Characteristics for Price Prediction
- Car Specifications and Pricing Dataset
- Automotive Sales Data for Machine Learning
Attributes
Original Data Source: Automobile Features and Price Analysis
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