# Caution!
**This competition had been wrongly started in public and had closed soon.**
We already have restarted another limited competition.

I want to delete this competition, but Kaggle says that they cannot delete any competition.

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# Context
This data set is about import cars assigned insurance risk rating in 1985.  The rating corresponds to the degree to which the auto is riskier than its price indicates.
      
Cars are initially assigned a risk factor symbol associated with its price.  Then, if it is riskier (or less), this symbol is adjusted by moving it up (or down) the scale.   Actuaries call this process "symboling".  A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.


# Content
The original data set is in the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Automobile).

This data set contains 205 instances (cars), each of them has 26 attributes: 15 continuous, 1 integer, and 10 nominal.  Some attributes have missing values.


# Training Data and Test Data
The training data is randomly chosen 144 (70%) instances and the test data is the remaining 61 (30%) instances.

# Notice
This is limited competition in [MPRG](http://mprg.jp/) and [1056Lab](https://1056lab.org), a data mining laboratory at [Chubu University](https://www3.chubu.ac.jp/main/english/), Japan.

# Reference
-[Jeffrey C. Schlimmer: Automobile Data Set](https://archive.ics.uci.edu/ml/datasets/Automobile)
-[Kibler, D., Aha, D.W., & Albert, M. (1989). Instance-based prediction of real-valued attributes. Computational Intelligence, Vol 5, 51--57.](https://doi.org/10.1111/j.1467-8640.1989.tb00315.x)