![]() To implement the Simple Linear regression model in machine learning using Python, we need to follow the below steps: In this section, we will create a Simple Linear Regression model to find out the best fitting line for representing the relationship between these two variables. ![]() How the dependent variable is changing by changing the independent variable.We will find the best fit line for the dataset.We want to find out if there is any correlation between these two variables. ![]() Here we are taking a dataset that has two variables: salary (dependent variable) and experience (Independent variable). Problem Statement example for Simple Linear Regression: (For a good model it will be negligible) Implementation of Simple Linear Regression Algorithm using Python The Simple Linear Regression model can be represented using the below equation:Ī0= It is the intercept of the Regression line (can be obtained putting x=0)Ī1= It is the slope of the regression line, which tells whether the line is increasing or decreasing. Such as Weather forecasting according to temperature, Revenue of a company according to the investments in a year, etc. Such as the relationship between Income and expenditure, experience and Salary, etc. Model the relationship between the two variables.Simple Linear regression algorithm has mainly two objectives: However, the independent variable can be measured on continuous or categorical values. The key point in Simple Linear Regression is that the dependent variable must be a continuous/real value. The relationship shown by a Simple Linear Regression model is linear or a sloped straight line, hence it is called Simple Linear Regression. Simple Linear Regression is a type of Regression algorithms that models the relationship between a dependent variable and a single independent variable. Next → ← prev Simple Linear Regression in Machine Learning
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