Summary  
This chapter explains how to implement polynomial regression by augmenting input features with higher-degree terms and solving for model parameters using the normal equation. It also shows how to extend this approach to multiple features for modeling more complex relationships.

General domain of usage  
Predictive modeling in machine learning

In the previous chapter, we explored quadratic regression, which has the graph of a parabola. In the same way, we could add the **x³** to the equation to get the **Cubic Regression** that has a more complex graph. We could also add **x⁴** and so on.

## A Degree of a Polynomial Regression
In general, it is called the **polynomial equation** and is the equation of **Polynomial Regression**. The highest power of **x** defines a **degree** of a Polynomial Regression in the equation. Here is an example

## N-Degree Polynomial Regression
Considering **n** to be a whole number greater than two, we can write down the equation of an **n**-degree Polynomial Regression.

## Normal Equation
And as always, parameters are found using the Normal Equation:

## Polynomial Regression with Multiple Features
To create even more complex shapes, you can use the Polynomial Regression with more than one feature. But even with two features, 2-degree Polynomial Regression has quite a long equation.

Most of the time, you won't need that complex model. Simpler models (like Multiple Linear Regression) usually describe the data well enough, and they are much easier to interpret, visualize and less computationally expensive.

Explores regression techniques for modeling and predicting continuous outcomes using Python. Builds on linear regression foundations to analyze relationships between variables, interpret model coefficients, and evaluate predictive performance on real-world datasets.
