## Basic concepts
**Regression** is one of the most popular supervised learning tasks.  
Its goal is to predict a numerical value (for example, the price of a house) called the **target**, given a set of parameters (size, age, location, etc.), which are called **features**. To train the model, you must provide many examples of such houses, both features and a target. The set of examples you train the model on is called the **training set**.

The simplest model capable of performing regression tasks is a **Linear Regression**.  
Let's look at the example of a Simple Linear Regression first.  
Consider this scatterplot displaying a person's height and his father's height.

## How it works
What Simple Linear Regression does is just fitting the straight line to the data so that the line is as close to the data points as possible. 

## Making the predictions
Now we can use this line to predict the target for a new point.  
For example, let's say you want to predict the person's height if his father is 63.5 inches tall. Just pick a point from the line that corresponds to X=63.5, and its y value is our prediction, easy peasy. 
The model predicts the person to be 64.3 inches tall.

## Simple Linear Regression Equation
As you may remember from school, the function of a line is **y=b+ax**, so during the training, simple linear regression just learns what values should **a** and **b** have to form a desired line. 
The values that the model learns are called **parameters**, and further in a course, we will denote parameters using **𝛽** instead of **a**, **b**.  
So our simple linear regression equation is:

In regression, the value we want to predict is called:

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

What Is The Linear Regression

Basic concepts

How it works

Making the predictions

Simple Linear Regression Equation

In regression, the value we want to predict is called:

Fill the gaps