---
title: "Cleansing the data set"
author: "Choonghyun Ryu"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Cleansing the data set}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r environment, echo = FALSE, message = FALSE, warning=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "")
```

## Preface
If you created a data set to develop a classification model, you must perform a cleansing of the data.
After you create the data set, you should do the following:

* **Cleansing the data set**
    + **Optional removal of variables, including missing values**
    + **Remove a variable with one unique number**
    + **Remove categorical variables with a large number of levels**
    + **Convert a character variable to a categorical variable**    
* Split the data into a train set and a test set
* Modeling and Evaluate, Predict

The alookr package makes these steps fast and easy:


## How to perform cleansing the data set

Refer to the following website for information on how to perform cleansing the data set.

- [`Cleansing the data set`](https://choonghyunryu.github.io/alookr_vignette/cleansing.html)



