https://github.com/pablo14/funModeling
R package: funModeling: data cleaning, importance variable analysis and model perfomance
https://github.com/pablo14/funModeling
Last synced: about 1 month ago
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R package: funModeling: data cleaning, importance variable analysis and model perfomance
- Host: GitHub
- URL: https://github.com/pablo14/funModeling
- Owner: pablo14
- License: mit
- Created: 2016-02-08T20:24:33.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2023-07-11T22:34:52.000Z (almost 2 years ago)
- Last Synced: 2024-07-31T19:25:31.990Z (9 months ago)
- Language: HTML
- Homepage: https://livebook.datascienceheroes.com
- Size: 8.96 MB
- Stars: 98
- Watchers: 11
- Forks: 29
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
Awesome Lists containing this project
- jimsghstars - pablo14/funModeling - R package: funModeling: data cleaning, importance variable analysis and model perfomance (HTML)
README
```{r setup, echo = FALSE}
knitr::opts_chunk$set(
fig.path = "man/figures/README-",
out.width = "200px"
)
```
[](https://cran.r-project.org/package=funModeling)
[](https://cran.r-project.org/package=funModeling)
# 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.