Simple Linear Regression - Machine Learning

SIMPLE LINEAR REGRESSION

About Linear Regression

Linear regression is an effective and commonly used machine learning algorithm that can be used to predict a continuous target variable based on one or more input features. This method assumes that there is a linear relationship between the input and output variables, and the goal of linear regression is to find a best-fit line that accurately represents this relationship. This line is determined by an intercept term and a slope coefficient, which are derived from the data.

There are two types of linear regression: simple and multiple. Simple linear regression predicts a target variable with only one input feature, while multiple linear regression uses multiple input features. The performance of the model is assessed by metrics such as mean squared error, mean absolute error, and R-squared.

Linear Regression

It is a basic and commonly used type of predictive analysis. These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables.

Y = a + bX 

where.

• Y – Dependent Variable
• a – intercept - Bise
• X – Independent variable
• b – Slope -Weights

Example Notebook

Let's consider the following notebook for simple Linear regression.

Not able to view notebook! Please use desktop view or open on github gist

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Conclusion

linear regression is a popular machine learning algorithm that is used to predict a continuous target variable based on one or more input features. It is a simple yet powerful algorithm that can provide useful insights and predictions for a variety of problems.

Overall, linear regression is a fundamental and essential tool in the machine learning toolkit and forms the foundation for many other machine learning algorithms.

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