Logistic Regression in Python
Logistic Regression in Python Tutorial
Logistic Regression is a statistical method of classification of objects. In this tutorial, we will focus on solving binary classification problem using logistic regression technique. This tutorial also presents a case study that will let you learn how to code and apply Logistic Regression in Python.
This tutorial has been prepared for students as well as professionals to gain a knowledge on performing Logistic Regression in Python.
This tutorial is written with an assumption that the learner is familiar with Python and its libraries, such as Pandas, Numpy, and Matplotlib. If you are new to Python or these libraries, we suggest you pick a tutorial based on them before you start your journey with Logistic Regression.
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Table of Contents
Chapter 1- Introduction
Chapter 2- Case Study
Chapter 3- Setting Up a Project
Chapter 4- Getting Data
Chapter 5- Restructuring Data
Chapter 6- Preparing Data
Chapter 7- Splitting Data
Chapter 8- Building Classifier
Chapter 9- Testing
Chapter 10- Limitations
Chapter 11- Summary
Chapter 1- Introduction
Logistic Regression is a statistical method of classification of objects. This chapter will give an introduction to logistic regression with the help of some examples.
To understand logistic regression, you should know what classification means. Let us consider the following examples to understand this better −
For many years, humans have been performing such tasks - albeit they are error-prone. The question is can we train machines to do these tasks for us with a better accuracy?
One such example of machine doing the classification is the email Client on your machine that classifies every incoming mail as “spam” or “not spam” and it does it with a fairly large accuracy. The statistical technique of logistic regression has been successfully applied in email client. In this case, we have trained our machine to solve a classification problem.
Logistic Regression is just one part of machine learning used for solving this kind of binary classification problem. There are several other machine learning techniques that are already developed and are in practice for solving other kinds of problems.
If you have noted, in all the above examples, the outcome of the predication has only two values - Yes or No. We call these as classes - so as to say we
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Publication Date: 03-26-2020
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