Logistic Regression

Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression).

What is Logistic Regression?
Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. To represent binary/categorical outcomes, we use dummy variables. You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using the log of odds as the dependent variable. In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function

Why Apply Logistic Regression?

Linear regression doesn’t give a good fit line for the problems having only two values(being shown in the figure), It will give less accuracy while prediction because it will fail to cover the datasets, being linear in nature.

For the best fit of categorical datasets, a Curve is being required which is being possible with the help of Logistic Regression, as it uses a Sigmoid function to make predictions