Goal of this article is to achieve following points:

- What is Normalization and Standardization ?
- Why we need Normalization and Standardization ?
- Advantages of Normalization and standardization ?

What is Normalization ?

Normalization :- This is process is commonly termed as column normalization as it is mostly applied column wise or on every feature of a dataset. This is one of the important process in data pre-processing before applying any operation or algorithm.

Procedure: , where is a particular value in a feature ,and is minimum value of that column , is the maximum value in that column .After doing this for all values in each column of dataset will lie in range [0,1].

**Advantages of Normalization:**

- Scaling of all the values without destroying the relationship between data.
- Getting rid of the calculation with very large values .

**Geometric interpretation of Normalization:**

What is Standardization

Standardization: It is a practice of making the mean of each column of data to zero and std-dev equal to1 . This is common practice in data cleaning process.By applying standardization it makes application of algorithm much more accurate.

The process of applying standardization:

Find :

- – Mean of the column on which standardization is to be applied
- – Std-dev of the column on which standardization is to be applied

Then replace with ,where is:-

=

**Advantages of Standardization:**

- Geometric interpretation of loss function gets more accurate
- the spread of the data is confined in range [-0.5,0.5].
- Squashing.

**Geometric interpretation of Standardization:**

Normalization and Standardization are very common practice in data cleaning process and they offer many advantages when comes to application of any machine learning algorithm over the cleaned data.