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Update README.md

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Car Evaluation/README.md
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This *beginner* level data set has 1728 rows and 7 columns.
Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods.
The data set could be used to evaluate cars according to the attributes listed in the data dictionary below.
This *intermediate* level data set was derived from a decision-making model which was originally developed for research on multi-attribute decision making.
Decision making involves selection between seemingly conflicting alternatives.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **classification modelling techniques**.
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 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,
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
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:
+ Marko Bohanec (marko.bohanec '@' ijs.si)
+ Blaz Zupan (blaz.zupan '@' ijs.si)
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  • Mr @harrymr672 commented

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  • John Harry @johnhary254 commented

    Thanks for sharing this database! It looks like a great resource for testing constructive induction and structure discovery methods. The hierarchical decision model approach seems particularly interesting. #Epos bihar.

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  • John Harry @johnhary254 commented

    Thanks for sharing! This dataset seems manageable for beginners. I’ll explore it further—appreciate the info! #tafcop.

  • John Harry @johnhary254 commented

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  • Gorilla @gorillafight684 commented

    Updated README.md with clearer instructions and improved formatting. Added 15 lines and removed 6 to enhance documentation clarity. the spike mod apk

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    Edited by Gorilla
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