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Car Attributes ML Dataset
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About
This collection of used car attributes is specifically structured for machine learning tasks, primarily focusing on regression analysis. It contains detailed information on vehicle specifications, physical dimensions, performance metrics, and pricing, offering a robust foundation for predicting car values or identifying key determinants of cost. The initial aim of this data was to provide a resource for users to practice different regression models and techniques.
Columns
The dataset includes more than 30 distinct features. Key attributes detailing the vehicles are:
- symboling: An assigned risk factor, typically ranging from -2 to 3.
- normalized-losses: Represents the relative average loss payment. Note that this column has a high percentage of missing values (18%).
- make: The manufacturer of the vehicle, with Toyota being the most frequent entry.
- body-style: Describes the shape of the car (e.g., sedan, hatchback).
- engine-type: Specifies the engine configuration (e.g., ohc).
- num-of-cylinders: The count of cylinders within the engine, most commonly four.
- horsepower: The maximum power output of the engine (mean value around 103).
- price: The target variable, showing vehicle prices ranging from 5118 to 45,400, with a mean of roughly 13.2k.
- city-mpg & highway-mpg: Fuel efficiency measurements in miles per gallon.
- physical dimensions: Includes
wheel-base,length,width, andheight. - normalized features: Includes scaled versions of length, width, and height.
Distribution
The data is currently available in a standard flat file format, typically CSV (
usedcars_dataset.csv). The structure consists of 33 columns and over 200 records. Specifically, there are 201 valid entries for most features. The file size is approximately 45.29 kB. Some columns, such as normalized-losses, bore, stroke, horsepower, and peak-rpm, have a small number of missing values (between 1% and 18%).Usage
This dataset is ideal for several analytical and modelling tasks, including:
- Regression Modelling Practice: The main intended use is to train and evaluate various regression algorithms, such as Linear Regression, to predict car prices.
- Feature Importance Analysis: Determining which vehicle attributes (e.g., engine size, horsepower, body style) have the greatest impact on the final sale price.
- Data Preparation Tutorials: Demonstrating techniques for handling missing data, normalizing features, and categorizing continuous variables (like
price_binned).
Coverage
The sources do not specify the geographic location or the exact time range from which the vehicle data was collected.
License
CC0: Public Domain
Who Can Use It
The data is suitable for a wide array of users:
- Machine Learning Engineers: Utilizing the structured features to build and tune advanced predictive price models.
- Academics and Students: Employing the data as a learning tool for foundational statistics, data visualization, and applied regression techniques.
- Automotive Market Analysts: Investigating how technical specifications and physical characteristics correlate with vehicle valuation.
Dataset Name Suggestions
- Used Vehicle Price Prediction Data
- Automobile Specifications for Regression
- Car Attributes ML Dataset
- Vehicle Pricing and Performance
Attributes
Original Data Source: Car Attributes ML Dataset
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