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# Linear Regression with Python

Linear Regression with Python

## 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: 1. In regression, the value we want to predict is called:
2. Fill the gaps

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

In the example, we predicted a person's height based on his father's height.
Then the father's height is a
_ _ _
.
The person's height is a
_ _ _
.
Previous records with other people's and their father's known heights form a
_ _ _
.

Click or drag`n`drop items and fill in the blanks  target  prediction  training set  feature

Everything was clear?

Section 1. Chapter 1