# Python | How and where to apply Feature Scaling?

**Feature Scaling or Standardization**: It is a step of Data Pre Processing that is applied to independent variables or features of data. It basically helps to normalize the data within a particular range. Sometimes, it also helps in speeding up the calculations in an algorithm.

**Package Used:**

sklearn.preprocessing

**Import:**

from sklearn.preprocessing import StandardScaler

**The formula used in Backend**

Standardization replaces the values with their Z scores.

Mostly the **Fit** method is used for Feature scaling

fit(X, y = None) Computes the mean and std to be used for later scaling.

## Python

`import` `pandas as pd` `from` `sklearn.preprocessing ` `import` `StandardScaler` `# Read Data from CSV` `data ` `=` `read_csv(` `'Geeksforgeeks.csv'` `)` `data.head()` `# Initialise the Scaler` `scaler ` `=` `StandardScaler()` `# To scale data` `scaler.fit(data)` |

**Why and Where to Apply Feature Scaling?**

The real-world dataset contains features that highly vary in magnitudes, units, and range. Normalization should be performed when the scale of a feature is irrelevant or misleading and not should Normalise when the scale is meaningful.

The algorithms which use Euclidean Distance measures are sensitive to Magnitudes. Here feature scaling helps to weigh all the features equally.

Formally, If a feature in the dataset is big in scale compared to others then in algorithms where Euclidean distance is measured this big scaled feature becomes dominating and needs to be normalized.

**Examples of Algorithms where Feature Scaling matters **

1. **K-Means** uses the Euclidean distance measure here feature scaling matters.

2. **K-Nearest-Neighbours** also require feature scaling.

3. **Principal Component Analysis (PCA)**: Tries to get the feature with maximum variance, here too feature scaling is required.

4. **Gradient Descent**: Calculation speed increase as Theta calculation becomes faster after feature scaling.

**Note:** Naive Bayes, Linear Discriminant Analysis, and Tree-Based models are not affected by feature scaling.

In Short, any Algorithm which is **Not** Distance-based is **Not** affected by Feature Scaling.