What is the importance of Data Scaling and Data Standardization?

Data Science, AI and ML
#1

DATA SCALING:

Scaling the target value is a good idea in regression modeling; scaling the data makes it easy for a model to learn and understand the problem. Scaling of the data comes under the set of steps of data pre-processing when we are performing machine learning algorithms in the data set.

Importance of Data Scaling:

Scaling the target value is a good idea in regression modeling; scaling the data makes it easy for a model to learn and understand the problem. Scaling of the data comes under the set of steps of data pre-processing when we are performing machine learning algorithms in the data set.

DATA STANDARDIZATION:

Data standardization is the process of rescaling the attributes so that they have meant as 0 and a variance of 1. The ultimate goal to perform standardization is to bring down all the features to a common scale without distorting the differences in the range of the values.

Importance of Data Standardization:

Data standardization helps improve the quality of your data by transforming and standardizing it. Think of it like a uniform for your databases. By taking this step, you are formatting your records in a way that creates consistency across your systems and makes it easy for businesses to use.