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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 –
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 -
What are the advantages of taking this course?
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?
Not for you? No problem.
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Certificate of completion.
|Section 1: Logistic Regression - Overview, Model Building, Assessment & Implementation|
1. Introduction to Predictive ModelingPreview
2. Logistic Regression OverviewPreview
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
Logistic Regression - Quiz
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!