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Regression  is a statistical way that examines the relationship among one or more independent variables (also known as predictors or explanatory variables) and a dependent variable (also known as the consequence variable or response variable). The goal of Regression  is to find the best fitting perfect that labels the relationship between the variables.

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There Are Many Different Types Of Regression , But The Most Common Are Linear, Logistic, And Nonlinear Regression .

Linear Regression  is used when the relationship amid the self-determining and dependent variables is assumed to be linear. This means that the alteration in the dependent variable is proportional to the alteration in the self-determining variable. Ex , we can use linear Regression  to predict the price of a house founded on its size and number of bedrooms.

Logistic Regression  is used when the reliant on variable is categorical, meaning that it can only take on a limited number of values, such as “yes” or “no”. ex, we can use logistic Regression  to predict whether a easy-going will have a heart attack based on their age, blood pressure, and cholesterol levels.

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