Why do we need logistic regression when we can use linear regression ?

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Accepted Answer

Logistic regression is used when the dependent variable is categorical, while linear regression is used for continuous variables.

Logistic regression predicts the probability of an event occurring, while linear regression predicts the value of a dependent variable.
Logistic regression uses a sigmoid function to map the output to a probability value between 0 and 1.
Linear regression assumes a linear relationship between the dependent and independent variables, while logistic regression does not.
For example, linear regression can be used to predict the price of a house based on its size and location, while logistic regression can be used to predict whether a customer will buy a product based on their age and income.

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