Logistic Regression using R in 10 Steps

Learn to build Logistic Regression model using R with a real life case study!
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  • Lectures 11
  • Length 1.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 4/2016 English

Course Description

Why Logistic Regression?

If you would like to become a data analyst/data scientist or take up a project on data analytics, then knowledge on predictive analytics is a key milestone as a large fraction of data analytics projects will be on predictive analytics.

Logistic Regression is one of the most commonly used predictive analytics techniques across domains like finance, healthcare, marketing, retail and telecom. It can help to predict the probability of occurrence of an event i.e. Logistic Regression can answer the questions like –

  • What is the probability that the customer will buy the product?
  • What is the probability that the debtor will pay back the loan?
  • What is the probability that your favorite team is going to win the match?
  • What is the probability that the employee will churn?

and so on…

What does this course cover?

This course covers logistic regression end-to-end using R in 10 steps, with a real life case study!

You will learn -

  • Data preparation
  • Model building
  • Model validation
  • Model assessment
  • Model implementation

What are the advantages of taking this course?

  • The course is completely done in R, an open source statistical language that is very popular among the data scientists today.
  • You will get the complete R code, dataset, data dictionary for the case study along with the lectures, as a part of this course.
  • This course will make equip you to take up a new Logistic Regression assignment on your own!

Who should enroll for this course?

Aspiring data analysts, students or any one keen on learning Logistic Regression from the basics

What are the prerequisites for this course?

Basic R

What are the requirements?

  • R/RStudio to be installed for this course
  • Basic Statistics

What am I going to get from this course?

  • Learn Logistic Regression using R in 10 steps!
  • Gain hands on experience in building Logistic Regression models using real life case studies
  • Understand the theory & statistics behind Logistic Regression technique
  • Learn how to interpret results from Logistic Regression model

Who is the target audience?

  • Statistics or Business Analytics students
  • Data Analysts working with financial institutions who would like to build credit/fraud prediction models
  • Data Analysts working with telecom, retail companies who would like to build customer propensity & churn models
  • Data Analysts or Data Scientists working with Analytics companies
  • Any other professionals who would like to build predictive models as part of their job
  • Aspiring Analytics professionals

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Logistic Regression - Overview, Model Building, Assessment & Implementation
1. Introduction to Predictive Modeling
2. Logistic Regression Overview
3. Case Study
4. Data Partitioning
5. Univariate Analysis
6. Bivariate Analysis
7. Multicollinearity Analysis
8. Model Building
9. Model Validation
10. Model Performance Assessment
11. Scorecard
Logistic Regression - Quiz
10 questions

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Instructor Biography

Aze Analytics, Learn Data Analytics!

We are a team of data scientists passionate about Analytics and keen on popularizing Analytics. We have been working with various Analytics companies and have a total work experience of around 15 years. Our projects include -

Customer Segmentation (Cluster Analysis)

Predictive Modeling (Logistic Regression, Linear Regression, Decision Tree and Neural Network)

Time Series Forecasting

Web Analytics and so on

We entered the teaching segment only a year back and is completely overwhelmed by the enthusiasm the students show to learn Analytics.

We believe that hands-on experience is very important when it comes to learning Data Analytics. So all our courses will be focused on case studies and will be using R, as R is open source and widely used by students & data scientists today!

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