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Saturday, 19 June 2021

Linear Regression vs Logistic Regression | Learn Data Science | Great learning






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Watch this tutorial on Linear Regression vs Logistic Regression! Linear Regression is a predictive model which is used for finding the linear relationship between a dependent variable and one or more independent variables, whereas logistic regression is a statistical method to model the probability for existing events. These are some of the most important predictive analysis algorithms. Whenever one wants to forecast or predict something, these regression algorithms are used for that purpose.

Great Learning brings you this tutorial on Linear Regression vs Logistic Regression to help you understand everything you need to know about this topic and to learn them well. This video starts off by explaining regression and its need, followed by understanding linear regression and logistic regression. Then we look at the differences between the two on the basis of a variety of criteria, such as their applications, dependable variable types, output, working, threshold value and so on. This video teaches Linear Regression, Logistic Regression and the key differences between both with a variety of demonstrations & examples to help you get started on the right foot.

00:00 Introduction
01:54 What is Regression?
04:41 Why is Regression needed?
07:16 What is Linear Regression?
13:32 What is Logistic Regression?
21:59 Linear Regression vs Logistic Regression
26:35 Practical Implementation in Python

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