In this project, you will learn about Logistic Regression and its application as Classification Algorithm. The project demonstrates the theoretical background of Logistic Regression using the Sigmoidal function. It also explains the suitability of linear vs logistic regression to answer the specific types of research questions. Finally, it covers an implementation of classification algorithm using logit model. The project utilizes the 'Candy' dataset for illustrative purpose.
Logistic Regression&application as Classification Algorithm
Taught in English
Instructor: Barsha Saha
Included with
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Recommended experience
What you'll learn
Create a Linear Regression model
Create a Logistic Regression model and compare with Linear model
Perform a classifcation task with Logit model
Skills you'll practice
Details to know
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction to Logistic Regression
Dataset and Linear Regression
Logistic Regression and comparison with Linear Regression
Classification Algorithm - Logit Model
Model Evaluation
Model Training
Model Testing
Recommended experience
Familiarity with Introductory Statistics and basic knowledge of R Studio is preferable
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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