funModeling

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R package: funModeling: data cleaning, importance variable analysis and model perfomance

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# Hello!

This package contains a set of functions related to exploratory data analysis, data preparation, and model performance. It is used by people coming from business, research, and teaching (professors and students).

## Books

`funModeling` is intimately related to the _Data Science Live Book_ -Open Source- (2017) in the sense that most of its functionality is used to explain different topics addressed by the book.

Versions:

* EN: [Data Science Live Book](https://livebook.datascienceheroes.com/)
* ES: [Libro Vivo de Ciencia de Datos](https://librovivodecienciadedatos.ai)

In the _Download_ section, you can buy (name your price) a digital copy of the book in PDF, mobi and pub.

## Blog posts based on `funModeling`:

* [Exploratory Data Analysis in R (introduction)](https://blog.datascienceheroes.com/exploratory-data-analysis-in-r-intro/)
* [Automatic data types checking in predictive models](https://blog.datascienceheroes.com/automatic-data-types-checking-in-predictive-models/)
* [Fast data exploration for predictive modeling](https://blog.datascienceheroes.com/fast-data-exploration-for-predictive-modeling/)
* [New discretization method: Recursive information gain ratio maximization](https://blog.datascienceheroes.com/discretization-recursive-gain-ratio-maximization/)

## Official page

* [funModeling official webpage](http://pablo14.github.io/funModeling/)
* Check the vignette [here](http://pablo14.github.io/funModeling/articles/funModeling_quickstart.html).

## If you speak Spanish...

You are invited to the [Escuela de Datos Vivos](https://escueladedatosvivos.ai/), a data school founded by the same funModeling / DSLB author. There you can find free and paid courses, blog post, youtube channel, using R and Python.

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