What is Linear Regression with examples?

Linear regression predicts a real-valued output based on input and is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It is a statistical method that is used for predictive analysis. It’s used to predict values within a continuous range, (e.g. share market, sales, salary, product price, etc.).

There are two categories

i. Simple regression
ii. Multivariable regression

Simple regression

Simple Linear Regression is characterized by one independent variable and It uses the traditional slope-intercept form, where m and b are the variables our algorithm will try to learn to produce the most accurate predictions.

x represents our input data and y represents our prediction.

y = mx+b

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