---
type: learning
area: learning
status: learning
date: 2026-05-03
created: 2026-05-03
updated: 1980-01-01
tags:
  - learning
---
**“Normalizing”** a vector most often means dividing by a norm of the vector. It also often refers to rescaling by the minimum and range of the vector, to make all the elements lie between 0 and 1 thus bringing all the values of numeric columns in the [dataset](https://towardsai.net/p/machine-learning/best-datasets-for-machine-learning-and-data-science-d80e9f030279 "datasets") to a common scale.
**“Standardizing”** a vector most often means subtracting a measure of location and dividing by a measure of scale. For example, if the vector contains random values with a Gaussian distribution, you might subtract the mean and divide by the standard deviation, thereby obtaining a “standard normal” [random variable](https://towardsai.net/p/statistics/bernoulli-distribution-probability-tutorial-with-python-90061ee078a "bernoulli distribution") with mean 0 and standard deviation 1.
### Why Should You Standardize / Normalize Variables:
#### Standardization:
For example, A variable that ranges between 0 and 1000 will outweigh a variable that ranges between 0 and 1. Using these variables without standardization will give the variable with the larger range weight of 1000 in the analysis. Transforming the data to comparable scales can prevent this problem. Typical data standardization procedures equalize the range and/or data variability.
#### Normalization:
For example, consider a data set containing two features, age, and income(x2). Where age ranges from 0–100, while income ranges from 0–100,000 and higher. Income is about 1,000 times larger than age. So, these two features are in very different ranges. When we do further analysis, like multivariate [linear regression](https://towardsai.net/p/machine-learning/calculating-simple-linear-regression-and-linear-best-fit-an-in-depth-tutorial-with-math-and-python-804a0cb23660 "linear regression"), for example, the attributed income will intrinsically influence the result more due to its larger value. But this doesn’t necessarily mean it is more important as a predictor. So we normalize the data to bring all the variables to the same range.
### When Should You Use Normalization And Standardization:
**Normalization** is a good technique to use when you do not know the distribution of your data or when you know the distribution is not Gaussian (a bell curve). Normalization is useful when your data has varying scales and the algorithm you are using does not make assumptions about the distribution of your data, such as [k-nearest neighbors](https://towardsai.net/p/machine-learning/k-nearest-neighbors-knn-algorithm-tutorial-machine-learning-basics-ml-ec6756d3e0ac "k-nearest neighbor") and artificial neural networks.
- Can suppress the effect of outliers. 
- MinMax Scalar is sensitive to outliers.
- ![[img-20240928-141755-e86530ff.png]]
**Standardization** assumes that your data has a Gaussian (bell curve) distribution. This does not strictly have to be true, but the technique is more effective if your attribute distribution is Gaussian. Standardization is useful when your data has varying scales and the algorithm you are using does make assumptions about your data having a Gaussian distribution, such as [linear regression](https://towardsai.net/p/machine-learning/calculating-simple-linear-regression-and-linear-best-fit-an-in-depth-tutorial-with-math-and-python-804a0cb23660 "linear regression"), logistic regression, and linear discriminant analysis.
![[img-20240928-174929-88e1766d.png]]
---
- Normalization scale data between (0,1). (MinMaxScaler)
- CNN and ANN use MinMAxScaler
![[img-20240928-180055-89edccb5.png]]
- Standardization based on standard normal distribution where mean = 0 and standard deviation is 1.(StandarScaler)
![[img-20240928-180138-5f2da79c.png]]
- KNN, K-measns, Linear regression, Logistic regression, All Deep Learning techniques we need to scale data. 
- Other Machine learning techniques we use Standardization