# New vtreat Documentation (Starting with Multinomial Classification)

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Nina Zumel finished some great new documentation showing how to use `Python`

`vtreat`

to prepare data for multinomial classification mode. And I have finally finished porting the documentation to `R`

`vtreat`

. So we now have good introductions on how to use `vtreat`

to prepare data for the common tasks of:

**Regression**:`R`

regression example,`Python`

regression example.**Classification**:`R`

classification example,`Python`

classification example.**Unsupervised data preparation**:`R`

unsupervised example,`Python`

unsupervised example.**Multinomial classification**:`R`

multinomial classification example,`Python`

multinomial classification example.

That is now 8 introductions to start with. To use `vtreat`

you only have to work through *one* introduction (the one helping with the task you have at hand in the language you are using).

As I have said before:

`vtreat`

helps with project blocking issues commonly seen in real world data: missing values, re-coding categorical variables, and dealing high cardinality categorical variables.- If you aren’t using a tool like
`vtreat`

in your data science projects: you are really missing out (and making more work for yourself).

To

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