Linear regression is a statistical model that, given a set of input features, attempts to fit the best possible straight line (or hyperplane, in the general case) between the independent and the dependent variable. Since its output is continuous and its cost function measures the distance from the observed to the predicted values, it is an appropriate choice to solve regression problems (e.g. to predict sales numbers).
Logistic regression, on the other hand, outputs a probability, which by definition is a bounded value between zero and one, due to the sigmoid activation function. Therefore, it is most appropriate to solve classification problems (e.g. to predict whether a given transaction is fraudulent or not).
