What is linear regression and logistics regression?

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Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.

Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.
Linear regression assumes a linear relationship between the independent and dependent variables, while logistic regression uses a logistic function to model the probability of the outcome.
Linear regression output is continuous and can take any value, while logistic regression output is a probability between 0 and 1.
Example: Linear regression can be used to predict house prices based on features like size and location, while logistic regression can be used to predict whether a customer will buy a product based on demographic data.

What is linear regression and logistics regression?

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