6-Polynomial Regression | Detailed Explanation on Polynomial Regression | ML for Non Tech

*What is Polynomial Regression?*
Polynomial regression is a type of regression analysis in which the relationship between the dependent variable and the independent variable is modelled as a polynomial. This approach is often used when the relationship between the dependent and independent variables is not linear.
Polynomial regression can be used to model a number of different relationships, including those that are non-linear. This type of regression is often used in situations where the dependent variable is not linearly related to the independent variable. For example, polynomial regression could be used to model the relationship between a dependent variable and an independent variable that is squared or cubed.
Polynomial regression can be used to model linear relationships as well as nonlinear relationships. Nonlinear relationships can be linearized by transforming the independent variable into a polynomial. The transformed independent variable is called a regressor.
The coefficients of the polynomial are estimated using least squares regression. The goodness of fit of the model can be assessed using the coefficient of determination.
Polynomial regression can be used for predictive modelling and forecasting. It can also be used for time series analysis.

*Polynomial Regression vs Linear Regression*
Polynomial regression is a type of linear regression in which the relationship between the dependent variable and the independent variable is not linear but rather is represented by a polynomial. For example, if we were trying to predict the height of a person based on their weight, we could use linear regression to model the relationship. However, if we wanted to model the relationship between the person's weight and their body fat percentage, we would need to use polynomial regression because the relationship is not linear.
There are several benefits of using polynomial regression over linear regression.
- Polynomial regression can model relationships that are nonlinear.
- Polynomial regression can provide a better fit to the data.
- Polynomial regression can be used to model interactions between variables.
- Polynomial regression is more flexible than linear regression

*Need for Polynomial Regression*
Polynomial regression is a type of regression algorithm that can be used when the relationship between the independent and dependent variables is nonlinear. This type of regression can be used to model a wide variety of situations, such as when the response of a system is not constant over time or when the effect of a change in an independent variable is not constant.
Polynomial regression is a powerful tool that can be used to model complex relationships between variables. However, it is important to remember that this type of regression is only an approximation of the true relationship between the variables. In some cases, the true relationship may be better represented by a different type of regression algorithm.

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