#### World's Best AI Learning Platform **with profoundly Demanding** Certification Programs

Designed by IITians, only for AI Learners.

Internship Partner

In Association with

In collaboration with

Designed by IITians, only for AI Learners.

Internship Partner

In Association with

In collaboration with

New to InsideAIML? Create an account

Employer? Create an account

Designed by IITians, only for AI Learners.

Internship Partner

In Association with

In collaboration with

Enter your email below and we will send a message to reset your password

Designed by IITians, only for AI Learners.

Internship Partner

In Association with

In collaboration with

By providing your contact details, you agree to our Terms of Use & Privacy Policy.

Already have an account? Sign In

Designed by IITians, only for AI Learners.

Internship Partner

In Association with

In collaboration with

By providing your contact details, you agree to our Terms of Use & Privacy Policy.

Already have an account? Sign In

Download our e-book of Introduction To Python

Matplotlib - Object-oriented InterfaceMatplotlib - MultiplotsCNTK - Creating First Neural NetworkMatplotlib - Working with ImagesMicrosoft Cognitive Toolkit (CNTK) - CPU and GPUPython Forensics - Memory and ForensicsPython Blockchain - Scope and ConclusionDiscuss Microsoft Cognitive ToolkitMatplotlib - Twin AxesMatplotlib - Subplot2grid() Function View More

Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) How can I write Python code to change a date string from "mm/dd/yy hh: mm" format to "YYYY-MM-DD HH: mm" format? How to choosing the right estimator for the machine learning problem? How to Write Python ZIP File? How to extracting text from PDF file using python How can a web interface execute a .py file from a PHP file? What methods can we use to differentiate between correlated and uncorrelated variables in a regression analysis? How to leave/exit/deactivate a Python virtualenvironment Join Discussion

4.5 (1,292 Ratings)

589 Learners

Sep 30th (7:00 PM) 1117 Registered

Shashank Shanu

2 years ago

- A step by step explanation of the Linear Regression Algorithm.

- What is Regression?

- Types of Regressions

- What is a Linear Regression?

1. Simple Linear Regression/ Univariate Linear Regression

1. The mathematics involved

2. How do we know this is the best fit line?

Hello Folks,

Hope you are well and staying safe at your place. As we all know how this
COVID-19 pandemic came and doesn't want to go from our life.

But as the whole world is fighting to get rid of this pandemic. I thought
why can't I share some things which I know so that many people may get benefits
from it.

So Let's start without wasting much time.

Before directly going deep into the Linear regression algorithm.

Let us first understand

Regression is a statistical technique that shows an algebraic relationship
between two or more variables.

Based on this algebric relationship (rather than a function), one can
estimate the value of a variable, given the values of the other variables.

Usually, correlation is used to check whether there is any relationship
between the two variables. If any relationship found, regression is used to
find the degree of relationships that can be then used for prediction.

Some of the examples are:

- Predict rainfall in cm for month
- Predict stock price for next day

Now as you got an idea about what is regression? Let’s move forward and
see what are the types of regressions?

- Linear regression
- Logistic regression
- Polynomial regression
- Stepwise regression
- Ridge regression
- Lasso regression
- ElasticNet regression

In this article I will explain you
about Linear Regression and later I will try to take you through the other
types of regressions.

Linear
regression performs the task to predict a dependent variable value (y) based on
a given independent variable (x). So, this regression technique finds out a
linear relationship between x (input) and y (output). Hence, the name is Linear
Regression.

In the figure above, X (input) is the work experience and Y (output) is the salary of a person. The regression line is the best fit line for our model.

In the figure above, X (input) is the work experience and Y (output) is the salary of a person. The regression line is the best fit line for our model.

Linear
Regression may further divided into

1. **Simple Linear Regression/ Univariate Linear
regression**

2. ** Multivariate Linear Regression **

When we try to find out a
relationship between a dependent variable (Y) and one independent (X) then it
is known as **Simple Linear Regression/ Univariate
Linear regression.**

The
mathematical equation can be given as:

Where

- Y is the response or the target variable
- x is the independent feature
- β1 is the coefficient of x
- β0 is the intercept

Let us consider Real-time
example

Let’s
suppose we have a dataset which contains information about the relationship between
‘a number of hours studied’ and ‘marks obtained’. Many students have been
observed and their hours of study and grade are recorded. This will be our
training data. The goal is to design a model that can predict marks if given the
number of hours studied. Using the training data, a regression line is obtained
which will give the minimum error. This linear equation is then used for any new
data. That is, if we give the number of hours studied by a student as an input, our
model should predict their mark with minimum error.

Like the Blog, then Share it with your friends and colleagues to make this AI community stronger.

To learn more about nuances of Artificial Intelligence, Python Programming, Deep Learning, Data Science and Machine Learning, visit our insideAIML blog page.

Keep Learning. Keep Growing