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Vehicle Quality Assessment Dataset
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About
This dataset is designed for predictive modelling and can be particularly useful for testing constructive induction and structure discovery methods. It features various attributes of cars to determine their evaluation level, making it suitable for classification tasks. The database is derived from a simple hierarchical decision model, offering insights into car characteristics and their overall assessment.
Columns
- buying: Categorical variable representing the buying price of the car.
- maint: Categorical variable indicating the price of the car's maintenance.
- doors: Categorical variable denoting the number of doors the car has.
- persons: Categorical variable describing the car's capacity in terms of persons it can carry.
- lug_boot: Categorical variable detailing the size of the luggage boot.
- safety: Categorical variable representing the estimated safety level of the car.
- class: Categorical target variable, which is the evaluation level of the car, with possible values including unacceptable, acceptable, good, and very good.
Distribution
The dataset is provided in a CSV format and contains 1728 records, organised across 7 columns. All variables are categorical, with no missing values. For example, the 'class' column, which is the target variable, shows that approximately 70% of the entries are categorised as 'unacc' (unacceptable), 22% as 'acc' (acceptable), with the remaining 8% distributed among other evaluation levels.
Usage
This dataset is ideal for machine learning practitioners and researchers interested in classification problems. It can be utilised for:
- Developing and testing algorithms for constructive induction.
- Exploring methods for structure discovery in hierarchical decision models.
- Building predictive models to classify car quality based on various features.
- Educational purposes, particularly for those new to classification tasks in data science.
Coverage
The dataset's scope is focused on car attributes and their evaluation. Specific geographic, time range, or demographic information is not available within the dataset description. The dataset is expected to be updated annually, ensuring its relevance over time.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Data Scientists and Machine Learning Engineers: For building and refining car evaluation classification models.
- Researchers: To experiment with new constructive induction and structure discovery techniques.
- Students and Educators: As an accessible "Beginner" level dataset for learning about classification, data cleaning, and model development in an automotive context.
- Automotive Analysts: To understand the factors contributing to car evaluations.
Dataset Name Suggestions
- Car Evaluation Criteria Dataset
- Automotive Feature Classification
- Vehicle Quality Assessment Dataset
- Car Attribute Evaluation Data
Attributes
Original Data Source: Vehicle Quality Assessment Dataset
QUALITY