Practical Guide to Logistic Regression Analysis in R

Introduction

Recruiters in analytics/data science industry expect you to know atleast two algorithms: Linear Regression and Logistic Regression. I believe you should have in-depth understanding of these algorithms. Why?

Due to their ease of interpretation, consultancy firms use these algorithms extensively. Startups are also catching up fast. As a result, in an analytics interview, most of the questions surface from linear and logistic regression topics.

In this article, we'll learn Logistic Regression in detail. Believe me, logistic regression isn't easy to master. It does follow some assumptions like linear regression. But, its method of calculating model fit, evaluation metrics are entirely different from linear/multiple regression.

But, don't worry! After you finish this tutorial, you'll become confident enough to explain logistic regression to your friends or even office colleagues. Alongside theory, we'll also learn to implement logistic regression on a data set. I'll use R Language. In addition, we'll also look at various types of logistic regression methods.

Table of Contents

1. What is Logistic Regression ?
2. What are the types of logistic regression techniques ?
3. How does Logistic Regression work ?
4. How to evaluate Logistic Regression's model fit and accuracy ?
5. Practical - Who survived on Titanic ?

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Note: This article is best suited for people who know basic algebra and have decent understanding of how linear regression works. If you don't know either, this article might not excite you !