# Feature Scaling – Standardization vs Normalization Explain in Detail

**What is Feature Scaling?**

•**Feature Scaling is a method to scale numeric features in the same scale or range (like:-1 to 1, 0 to 1).**

•**This is the last step involved in Data Preprocessing and before ML model training.**

•**It is also called as data normalization.**

•**We apply Feature Scaling on independent variables.**

•**We fit feature scaling with train data and transform on train and test data.**

**Why Feature Scaling?**

•**The scale of raw features is different according to its units.**

•**Machine Learning algorithms can’t understand features units, understand only numbers.**

•*Ex: If hight 140cm and 8.2feet*

•*ML Algorithms understand**numbers then 140 > 8.2*

**Which ML Algorithms Required Feature Scaling?**

**Those Algorithms Calculate Distance **

•**K-Nearest Neighbors (KNN)**

•**K-Means**

•**Support Vector Machine (SVM)**

•**Principal Component Analysis(PCA)**

•**Linear Discriminant Analysis**

**Gradient Descent Based Algorithms **

•**Linear Regression,**

•**Logistic Regression**

•**Neural Network**

**Tree Based Algorithms not required Feature scaling**

•**Decision Tree, Random Forest, XGBoost**

**Types of Feature Scaling**

•**1) Min Max Scaler**

•**2) Standard Scaler**

•**3) Max Abs Scaler**

•**4) Robust Scaler**

•**5) Quantile Transformer Scaler**

•**6) Power Transformer Scaler**

•**7) Unit Vector Scaler**

**Standardization vs Normalization Explain in Detail**

**What is Standardization?**

•**Standardization rescale the feature such as** **mean(μ) = 0 and standard deviation (σ) = 1.**

•**The result of standardization is Z called as Z-score normalization.**

• **If data follow a normal distribution (gaussian distribution).**

• **If the original distribution is normal, then the standardized distribution will be normal.**

• **If the original distribution is skewed, then the standardized distribution of the variable will also be skewed.**

** **What is Normalization?

•**Normalization rescale the feature in fixed range between 0 to 1.**

•**Normalization also called as Min-Max Scaling.**

•**If data doesn’t follow normal distribution (Gaussian distribution).**

**Standardization vs Normalization**

•**There is no any thumb rule to use Standardization or Normalization for special ML algo.**

•**But mostly Standardization use for clustering analyses, Principal Component Analysis(PCA).**

**•Normalization prefers for Image processing because of pixel intensity between 0 to 255, neural network algorithm requires data in scale 0-1, K-Nearest Neighbors.**

### Download Practical Source code

**More reading material in detail**

For a beginner ==> Click here

For a beginner & Professional==> Click here

#### Scikit-Learn official material

Standerd Scaler ==> Click Here

Min Max Scaler ==> Click Here