Logistic regression is a part of the supervised learning category; it measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic/sigmoid function. In spite of the name'logistic regression,' this is not used for regression problem where the task is to predict the real-valued output. It is a classification problem which is used to predict a binary outcome (1/0, -1/1, True/False) given a set of independent variables. In linear regression, you predict a real-valued output y based on a weighted sum of input variables as shown below. The aim of linear regression is to estimate values for the model coefficients c, w1, w2, w3 ….wn and fit the training data with minimum error to predict the output y.