What is linear regression in machine learning

Linear regression is a statistical method that is used for predictive analysis. It is used to predict sales, salary, prices, age, etc.

Linear regression shows a linear relationship between the dependent variable (Y) and the independent variable (x). It tells us how the value of dependent variables changes according to the value of the independent variable.

Linear Regression Equation

Y= mx+c

Y= Dependent Variable

x = Independent Variable

m = Slope of the line

c = Intercept (Tells at what point the line will cut the Y-axis)

Types Linear Regression

1. Simple Linear Regression: It uses single independent variables.

2. Multiple Linear Regression: It uses more than one independent variable.

Linear Regression Lines

A line showing the relationship between dependent and independent variables is called a regression line. Is is of two types.

1) Positive Linear Relationship:

If a dependent variable increase on Y-axis and an independent variable increases on X-axis, then such a relationship is called Positive Linear Relationship.

2) Negative Linear Relationship:

If a dependent variable decreases on the Y-axis and independent variable increases on the X-axis, then such relationship is called a negative linear relationship.

Let's understand some concept.

As the va;ue of C increases, line shifts upwards but angle of the line does not changes.

If the value of M increases, the rate at which the value of Y increases with respect to X also increases.

This post is all about the theory, in the coming posts we will do a project and also see how to optimise it using the concept of gradient descent.