Linear Regression and Logistics Regression and difference between both.

Question

Accepted Answer

Linear Regression predicts continuous values while Logistic Regression predicts binary outcomes.

Linear Regression is used for predicting continuous values while Logistic Regression is used for predicting binary outcomes.
Linear Regression uses a linear approach to model the relationship between dependent and independent variables while Logistic Regression uses a logistic function to model the probability of a binary outcome.
Linear Regression assumes a normal distribution of errors while Logistic Regression assumes a binomial distribution of errors.
Example of Linear Regression: predicting the price of a house based on its size, location, and other features. Example of Logistic Regression: predicting whether a customer will buy a product or not based on their demographic information.

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