In this tutorial, we will learn about binary logistic regression and its application to real life data using Python. We have also covered binary logistic regression in R in another tutorial. Without a doubt, binary logistic regression remains the most widely used predictive modeling method. Logistic Regression is a classification algorithm that is used to predict the probability of a categorical dependent variable. The method is used to model a binary variable that takes two possible values, typically coded as 0 and 1

## Binary Logistic Regression – a tutorial

In this tutorial we’ll learn about** binary logistic regression** and its application to real life data. Without any doubt, binary logistic regression remains the most widely used predictive modeling method.

## Binary Logistic Regression with R – a tutorial

In a previous tutorial, we discussed the concept and application of **binary logistic regression**. We’ll now learn more about binary logistic regression model building and its assessment using R.

Firstly, we’ll recap our earlier case study and then develop a binary logistic regression model in R. followed by and explanation of **model sensitivity** and **specificity**, and how to estimate these using R.