📈 Polynomial Regression

Polynomial regression is an extension of linear regression where we model a nonlinear relationship between input $𝑥$ and output $𝑦$ by adding powers of $𝑥$.

Sometimes by defining a new feature you might get a better Model that requires less computation

Use case:

When prediction fits Polynomial equation instead of linear equation

For example house price can defined by calculating area instead of creating 2 variable equation we can define one variable equation :

• Scaling of feature becomes crucial in Polynomial Regression
• Some algo can choose feature to fit polynomial curves
• Feature engineering is the process of creating new features from existing ones to improve model performance.

Generic polynomial regression model of degree n:

$y = \theta_0 + \theta_1 x + \theta_2 x^2 + \theta_3 x^3 + ... + \theta_n x^n$

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