## n-feature Linear Regression Equation
As we have seen, adding the new feature to the linear regression model is as easy as adding it along with the new parameter to the model's equation. We can add much more than two parameters that way.  
> Note
>
> Consider **n** to be a whole number greater than two.

## Normal Equation
The only problem is the visualization. If we have two parameters, we need to build a 3D plot. But if we have more than two parameters, the plot will be more than three-dimensional. But we live in a 3-dimensional world and cannot imagine higher-dimensional plots. However, it is not necessary to visualize the result. We only need to find the parameters for the model to work. Luckily, it is relatively easy to find them. The good old Normal Equation will help us:

## X̃ Matrix
Notice that only the **X̃**  matrix has changed. Let's take a closer look at this matrix. You can think of the columns of this matrix as each responsible for its **β** parameter. The following video explains what I mean.

The first column of 1s is needed to find the **β₀** parameter.

Linear Regression is a crucial concept in predictive analytics. It is widely used by data scientists, data analytics, and statisticians as it is easy to build and interpret but powerful enough for many tasks.

Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.

Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.

A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.

Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.

Linear Regression with Python

Linear Regression with n Features

n-feature Linear Regression Equation

Normal Equation

X̃ Matrix