# Linear Regression Machine Learning | Examples

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## Linear Regression-

In Machine Learning,

• Linear Regression is a supervised machine learning algorithm.
• It tries to find out the best linear relationship that describes the data you have.
• It assumes that there exists a linear relationship between a dependent variable and independent variable(s).
• The value of the dependent variable of a linear regression model is a continuous value i.e. real numbers.

Also Read- Machine Learning Algorithms

## Representing Linear Regression Model-

Linear regression model represents the linear relationship between a dependent variable and independent variable(s) via a sloped straight line.

The sloped straight line representing the linear relationship that fits the given data best is called as a regression line.

It is also called as best fit line.

## Types of Linear Regression-

Based on the number of independent variables, there are two types of linear regression-

1. Simple Linear Regression
2. Multiple Linear Regression

## 1. Simple Linear Regression-

In simple linear regression, the dependent variable depends only on a single independent variable.

For simple linear regression, the form of the model is-

Y = β0 + β1X

Here,

• Y is a dependent variable.
• X is an independent variable.
• β0 and β1 are the regression coefficients.
• β0 is the intercept or the bias that fixes the offset to a line.
• β1 is the slope or weight that specifies the factor by which X has an impact on Y.

There are following 3 cases possible-

### Case-01: β1 < 0

• It indicates that variable X has negative impact on Y.
• If X increases, Y will decrease and vice-versa.

### Case-02: β1 = 0

• It indicates that variable X has no impact on Y.
• If X changes, there will be no change in Y.

### Case-03: β1 > 0

• It indicates that variable X has positive impact on Y.
• If X increases, Y will increase and vice-versa.

## 2. Multiple Linear Regression-

In multiple linear regression, the dependent variable depends on more than one independent variables.

For multiple linear regression, the form of the model is-

Y = β0 + β1X1 + β2X2 + β3X3 + …… + βnXn

Here,

• Y is a dependent variable.
• X1, X2, …., Xn are independent variables.
• β0, β1,…, βn are the regression coefficients.
• βj (1<=j<=n) is the slope or weight that specifies the factor by which Xj has an impact on Y.

To gain better understanding about Linear Regression,

Watch this Video Lecture

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Summary
Article Name
Linear Regression Machine Learning | Examples
Description
Linear Regression in Machine Learning is a supervised machine learning algorithm that finds out the best linear relationship describing the data you have. Types of Linear Regression- Simple Linear Regression & Multiple Linear Regression.
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Gate Vidyalay
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