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# Normalization vs Standardization, When to Use What?

`Normalization` adjusts values to a `0-1 range` to ensure that no single value has an outsized influence on the result. `Standardization` scales data so that it has a mean of `0` and a standard deviation of `1`, often resulting in a normal distribution.

In this lesson, you'll learn the differences between normalization and standardization and the appropriate use cases for each.  

<br />

## How Do Normalization and Standardization Differ?

| Criterion          | Normalization  | Standardization  |
|-------------------|------------------------|--------------------------|
| Transformation Method    | Adjustment to 0-1 range (Min-Max Scaling) | Adjustment to mean 0, standard deviation 1 (Z-score Scaling) |
| Sensitivity to Outliers  | Sensitive to outliers | Less sensitive to outliers |
| Use Cases    | Image processing, deep learning | Statistical analysis, regression, PCA |
| Applicability    | When the range of values matters | When data follows a normal distribution |

<br />

## How to Choose Between Normalization and Standardization

Both techniques have their own advantages and should be selected based on the data’s characteristics and the analysis goal.

Here’s how to decide between normalization and standardization in practical scenarios.

<br />

### If You Are Doing Deep Learning  
→ `Normalization` is generally more favorable (neurons learn more stably within the 0-1 range)  

### If You Are Performing Statistical Analysis  
→ `Standardization` is suitable (adjusts data to distribute around the mean)  

### If There Are Many Outliers in the Data  
→ `Standardization` is preferable (extreme values have less impact)  

### If Maintaining the Range of Data Is Important  
→ `Normalization` is used (maintains the minimum-maximum range)  

<br />

In the next lesson, we'll explore `categorical data encoding`.

Normalization scales data to a range between 0 and 1, but it can be sensitive to outliers. Standardization, which uses the mean and standard deviation, is often more robust in such cases.

### Which word best completes the sentence?

Criterion	Normalization	Standardization
Transformation Method	Adjustment to 0-1 range (Min-Max Scaling)	Adjustment to mean 0, standard deviation 1 (Z-score Scaling)
Sensitivity to Outliers	Sensitive to outliers	Less sensitive to outliers
Use Cases	Image processing, deep learning	Statistical analysis, regression, PCA
Applicability	When the range of values matters	When data follows a normal distribution