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# Data Transformation in R – How to standardize Data in R

Standardizing data in R is a way to make sure that all the variables in a dataset have the same scale. This is important because some machine learning algorithms are sensitive to the scale of the variables and can perform better when the data is standardized. The process of standardizing data involves subtracting the mean of a variable from each value and then dividing by the standard deviation. In R, you can use the scale() function to standardize data. This function takes a dataset as an argument and returns a new dataset with the standardized values. It’s important to note that before standardizing the data, it’s a good idea to check for missing values and outliers and handle them appropriately. Once the data is standardized, you can use it as input for various machine learning models.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Data Transformation in R – How to standardize Data in R.

## Data Transformation in R – How to standardize Data in R

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

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