In the Regression problem, it is assumed that the relationship between independent variables and the dependent variable is linear. But it may be possible that the **relationship is non-linear**.

When the relationship is non-linear, creating a straight line may not be the best decision boundary, we need to have a decision boundary that can fit the data well. Hence higher polynomial degree features are included in the model to make a non-linear decision boundary.

Polynomial Regression is also used to solve the problem of **Underfitting.**

**Polynomial regression is a special kind of Linear Regression that includes the feature of n polynomial degree.**

where

**y =**predicted output**b0 =**intercept, bias**x1 =**independent variable- = second polynomial degree of feature 1
- = third polynomial degree of feature 1
- = n polynomial degree of feature 1
**b1,b2,..,bn =**coefficient of corresponding features**n**polynomial degree is the highest degree feature present.