What do you understand by linear regression and logistic regression?
In statistics, linear regression is a linear approach to modelling the relationship between a dependent variable(y) and one or more independent variables(X). In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Linear Regression is one of the most popular algorithms in Machine Learning. That’s due to its relative simplicity and well known properties.
It’s a classification algorithm, that is used where the response variable is categorical . The idea of Logistic Regression is to find a relationship between features and probability of particular outcome .
E.g. When we have to predict if a student passes or fails in an exam when the number of hours spent studying is given as a feature, the response variable has two values, pass and fail.