Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.What is Linear Regression | Simple Linear Regression
Linear Regression with Python
course content

Course Content

Linear Regression with Python

Linear Regression with Python

1. Simple Linear Regression
What is Linear RegressionFinding the ParametersBuilding Linear Regression Using NumPyBuilding Linear Regression Using StatsmodelsPredict House Prices
2. Multiple Linear Regression
Linear Regression with Two FeaturesLinear Regression with n FeaturesBuilding Multiple Linear RegressionChoosing the FeaturesPredict Prices Using Two Features
3. Polynomial Regression
Quadratic RegressionPolynomial RegressionBuilding Polynomial RegressionInterpolation vs ExtrapolationEvaluate the Model
4. Choosing The Best Model
MetricsOverfittingR-squaredPredict Prices Using Polynomial Regression

bookWhat is Linear Regression

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:

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

Select the correct answer

question-icon

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

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 1
We're sorry to hear that something went wrong. What happened?
some-alt