We are starting this course with a case study. So, learn practically. Learn with case studies on Advertisement Dataset, Diabetes Dataset, Credit Risk using Logistic Regression in R Studio. Regression is a statistical method which helps to determine the relationship between one dependent variable and other independent variables. It explains how the dependent variable changes when one of the independent variable varies. It is also used to know which independent variable is related to the dependent variable and what is their relationship. Regression analysis is widely used in the field of prediction and forecasting. Regression analysis is an important component for modelling and analyzing data. Regression is of two types – Linear regression and Multiple regression. Linear regression uses one independent variable to know the outcome whereas Multiple regression uses two or more independent variable to forecast the output. In the recent years many techniques have been developed to perform regression analysis. They are Linear regression, Logistic regression, Polynomial regression, Stepwise regression, Ridge regression, Lasso Regression and Elastic net regression. Uses of regression analysis Logistic regression in R is defined as the binary classification problem in the field of statistic measuring. The difference between a dependent and independent variable with the guide of logistic function by estimating the different occurrence of the probabilities, i.e., it is used to predict the outcome of the independent variable (1 or 0 either yes/no) as it is an extension of a linear regression which is used to predict the continuous output variables. How does Logistic Regression in R works? Logistic regression is a technique used in the field of statistics measuring the difference between a dependent and independent variable with the guide of logistic function by estimating the different occurrence of probabilities. They can be either binomial (has yes or No outcome) or multinomial (Fair vs poor very poor). The probability values lie between 0 and 1, and the variable should be positive (<1). It targets the dependent variable and has the following steps to follow: Tags: Data ScienceDevelopmentLogistic Regression