Linear Regression

In Machine Learning

Linear regression creates a relationship between a dependent variable (the one we want to predict) and one or more independent variables by assuming a linear relationship. It uses a linear equation to model this relationship.
Linear regression is a supervised machine learning algorithm that learns from labeled datasets. It generates predictions based on the most optimized linear function retrieved through training.

• 1. What is linear regression examples?

"Predicting house prices based on square footage, estimating exam scores from study hours, and forecasting sales using advertising spending are examples of linear regression applications."

• 2. Why is it called linear regression?

"Linear regression is named for its use of a linear equation to model the relationship between variables, representing a straight line fit to the data points."

• 3. How to use linear regression?

"Use linear regression by fitting a line to predict the relationship between variables, understanding coefficients, and making predictions based on input values for informed decision-making."

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References:GeeksForGeeks