This *beginner* level data set has 1728 rows and 7 columns.
This *intermediate* level data set was derived from a decision-making model which was originally developed for research on multi-attribute decision making.
Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods.
Decision making involves selection between seemingly conflicting alternatives.
The data set could be used to evaluate cars according to the attributes listed in the data dictionary below.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **classification modelling techniques**.
The data set has 1728 rows and 7 columns in which car attributes such as price and technology are described across 6 attributes such as "Buying Price", "Maintenance", and "Safety" etc. There are multiple alternatives under each of the 6 attributes. Car's acceptability,
Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
the seventh attribute, is the outcome variable.
This data set is recommended for learning and practicing your skills in **classification modelling techniques**. Feel free to explore the data set with multiple
classification methods. The Following data dictionary gives more details on this data set:
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### Acknowledgement
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Car Evaluation Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Car+Evaluation).
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Car Evaluation Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Car+Evaluation). The UCI page mentions following as the donor of the dataset:
