Uplift Modeling Made Easy

Develop an uplift / incremental response model with interaction approach using a real-world modeling project as example
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  • Lectures 28
  • Length 2 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 7/2014 English

Course Description

In this class, I will show you how to develop an uplift / incremental response model through hands-on training.

In this 2-hr training course, I will walk you through the modeling approach and show you how to develop and validate the model step-by-step. Together, we will solve a real-world modeling project using SAS software. I will provide extensive demo on SAS coding, running codes, and explaining outputs for each step. All SAS codes and modeling data will be made available to you as well.

The goal for this training is for you to be able to develop a professional-level uplift / incremental model independently.

What are the requirements?

  • Microsoft Excel and PowerPoint or the similar softwares
  • SAS Software with Base/Stat/Graph Components

What am I going to get from this course?

  • Learn how to develop an uplift response model from the beginning to the end
  • Learn how to interpret modeling results
  • Learn how to present modeling results to others

What is the target audience?

  • Working professionals who want to learn predictive modeling skills
  • Graduates or undergraduates who want to explore their career opportunities in advanced analytics

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.

Curriculum

Section 1: Introduction
Course objectives, course outlines, and pre-requisites
Preview
05:34
What is uplift modeling, its basic concepts, and its applications
Preview
06:29
Section 2: Project
03:05

Click the following link to download the modeling data set:

https://s3.amazonaws.com/AnalyticsOnlineCourses/Uplift/data/uplift_data.sas7bdat

Section 3: Data
Modeling data, variables, and data snapshot
06:53
Section 4: Methodology
Uplift modeling methodology overview - Part 1
04:41
Uplift modeling methodology overview - Part 2
04:46
Uplift modeling methodology overview - Part 3
Preview
04:11
Section 5: Modeling
Modeling process overview
02:31
05:30

Please save the sas codes 'Uplife Model Step 1.txt' as .sas file in order to run in sas software.

Please click the following link to download the sas macro:

https://s3.amazonaws.com/AnalyticsOnlineCourses/Uplift/code/sasmacr.sas7bcat

Step 1 - prepare data - data QC demo - Part 2
06:52
Step 1 - prepare data - data QC demo - Part 3
03:47
Step 1 - prepare data - data transformation demo
04:01
05:26

Please save sas code as .sas in order for you to run in sas software

Step 2 - variable reduction demo - Part 2
04:06
07:20

Please save sas codes as .sas format so that you can run the code in sas software.

Step 3 - deep dive analysis demo - Part 2
04:28
Step 3 - deep dive analysis demo - Part 3
06:03
04:18

Please save the sas codes as .sas format. So that you can run them in sas software.

Step 4 - variable transformations demo - Part 2
05:04
Step 4 - variable transformations demo - Part 3
04:56
Step 4 - variable transformations demo - Part 4
04:32
04:44

Please save sas code as .sas format. So that you can run them in sas software.

Step 5 - develop and validate model demo - Part 2
04:34
Step 5 - develop and validate model demo - Part 3
05:26
Step 5 - develop and validate model demo - Part 4
04:39
Step 5 - develop and validate model demo - Part 5
04:33
Section 6: Results Summary
Modeling result presentation
03:25
Section 7: Downloadable SAS Codes
Article

Please download and save as .sas in order to run in SAS software

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Instructor Biography

Predictive Analytics, Advanced Analytics Made Simple

15+ years of professional experience on predictive modeling in different industries. Specialized in logistic regression, linear regression, generalized regression, survival analysis, decision tree, discriminant analysis, cluster analysis, and other advanced analytic. In-depth knowledge and experiences in customer acquisition, retention, and credit risk management. Developed training classes and taught classes to graduate students and other working professionals.

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