Before studying logistic regression, I would recommend you to go through these tutorials.

The first and most important thing about logistic regression is that it is not a "Regression" but a "Classification" algorithm. The name itself is somewhat misleading. Regression gives a continuous numeric output but most of the time we need the output in classes (i.e. categorical, discrete). For example, we want to classify emails into "spam" or "not spam", classify treatment into "success" or "failure", classify statement into "right" or "wrong" , classify transactions into "fraudulent" or "non-fraudulent" and so on. These are the examples of logistic regression having binary output (also called dichotomous). Note that the output may not always be binary but in this article I merely talk about binary output.

The first and most important thing about logistic regression is that it is not a "Regression" but a "Classification" algorithm. The name itself is somewhat misleading. Regression gives a continuous numeric output but most of the time we need the output in classes (i.e. categorical, discrete). For example, we want to classify emails into "spam" or "not spam", classify treatment into "success" or "failure", classify statement into "right" or "wrong" , classify transactions into "fraudulent" or "non-fraudulent" and so on. These are the examples of logistic regression having binary output (also called dichotomous). Note that the output may not always be binary but in this article I merely talk about binary output.