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In this post, we will understand the difference between linear regression and logistic regression.

It helps predict the variable that is continuous, and is a dependent variable.

This is done using a given set of independent variables.

It extrapolates a line to find the value of dependent variable.

Least square methods are used to estimate the accuracy.

The best fit line is found, that helps predict the output.

It is generally a continuous value.

The relation between the dependent variable and independent variable has to be linear.

The independent variables may have collinearity between them.

It is considered a machine learning problem, i.e an applied statistics problem.

It helps predict categorical variables.

It is discrete value.

It helps solve classification problems.

It uses the sigmoid function, which is in the form of an ‘S’, to classify the data examples.

It uses Maximum likelihood estimation to predict values.

Its output includes values like 0, 1, Yes, No, True, False.

It doesn’t require the dependent and independent variable to have a linear relationship.

There shouldn’t be any collinearity between the independent variables.

It is considered a machine learning problem, i.e an applied statistics problem.

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