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Car Acceptability Evaluation Model
Data Science and Analytics
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About
Evaluates Car acceptability using a hierarchical decision model. Originally developed to test the HINT (Hierarchy INduction Tool), it assesses whether a car is considered unacceptable, acceptable, good, or very good based on six key attributes. The model provides a structured framework for multi-attribute decision-making in the context of vehicle evaluation.
Columns
- buying: The buying price of the car, categorised as vhigh, high, med, or low.
- maint: The maintenance cost of the car, categorised as vhigh, high, med, or low.
- doors: The number of doors, ranging from 2 to 5.
- persons: The passenger capacity of the car, noted as 2, 4, or more.
- lug_boot: The size of the luggage boot, categorised as small, med, or big.
- safety: The estimated safety level of the car, categorised as low, med, or high.
- class: The final evaluation level, categorised as unacceptable (unacc), acceptable (acc), good, or very good.
Distribution
The dataset is provided in a CSV file format and contains 1726 records across 7 columns. There are no missing or mismatched values. It is expected to be updated quarterly.
Usage
This dataset is ideal for tasks in machine learning and data analysis. It can be used for training and evaluating classification models, particularly those based on hierarchical decision-making. Researchers and students can use it to compare the performance of different algorithms, such as C4.5 and HINT, in reconstructing decision models. It is also a valuable resource for teaching multi-attribute decision-making concepts.
Coverage
The dataset is conceptual and does not represent a specific geographic region, time period, or demographic group. It is a generalised model for evaluating car acceptability based on a predefined set of features.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For building and testing classification algorithms to predict car acceptability.
- Machine Learning Students: As a practical dataset for learning about decision trees, hierarchical models, and function decomposition.
- Automotive Analysts: To understand the key factors that contribute to a car's perceived value and acceptability.
- Educators: For demonstrating principles of multi-attribute decision-making and knowledge acquisition in expert systems.
Dataset Name Suggestions
- Car Acceptability Evaluation Model
- Hierarchical Car Evaluation Data
- Automobile Purchase Decision Factors
- Vehicle Acceptability Classifier Dataset
Attributes
Original Data Source: Car Acceptability Evaluation Model
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