Market Basket Analysis & Linear Discriminant Analysis with R
4.3 (48 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
300 students enrolled

Market Basket Analysis & Linear Discriminant Analysis with R

Master: Association rules (MBA) & it's usage, Linear Discriminant Analysis (LDA) for classification & variable selection
4.3 (48 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
300 students enrolled
Last updated 8/2017
English
English
Current price: $11.99 Original price: $19.99 Discount: 40% off
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This course includes
  • 3.5 hours on-demand video
  • 17 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll learn
  • Students will know what is association rules (Market Basket Analysis)?
  • How do association rules work?
  • How to do market basket analysis using Excel & R
  • What is linear discriminant analysis?
  • How to do linear discriminant analysis using R?
  • How to understand each component of the linear discriminant analysis output?
  • Practical usage of linear discriminant analysis
Course content
Expand all 36 lectures 03:24:28
+ Part 1 - Association Rules (Market Basket Analysis)
9 lectures 37:45
How to study this course?
01:23
What is Market Basket Analysis (MBA) / Association rules ?
04:44

How it can be applied in a variety of situations

Usage of Association Rules
05:55
How does an association rule look like?
05:45
Strength of an association rule - Confidence measure
03:21
Strength of an association rule - Lift measure
05:52
Basic Algorithm to derive rules
05:32
+ Part 1- Association rules demo & quiz
5 lectures 27:49

Discussion on breadth first algorithm (BFS) and depth first algorithm (DFS)

Demo of Basic Algorithm to derive rules (BFS and DFS)
06:01

Revisit understanding of strength of association rules

Demo Using R on Fruit transaction data
09:25
Demo Using R on another transaction data
07:11
Try your learning - assignment
02:01
Revisit your learning
10 questions
Assignment solution
03:11
+ Part 2 - Linear Discriminant Analysis (LDA)
8 lectures 51:54
Need of a classification model
08:02
Purpose of Linear Discriminants
03:11
A case for classification
07:10
Formal definition of LDA
10:33
Analytics techniques applicability
02:45
First practical use of LDA - LDA for Variable Selection
06:42
Demo of using LDA for Variable Selection
11:10
+ Part 2 : Second practical usage of LDA - LDA for classification
14 lectures 01:27:00

-Second practical usage of LDA

-Understand which are three important component to understand LDA properly

Preview 01:59
First complexity : distance calculation - Euclidean distance
06:54
First complexity : distance calculation (enhanced) - Mahalanobis distance 01
05:22
First complexity : distance calculation (enhanced) - Mahalanobis distance 02
01:59
Second complexity : Linear Discriminant Function
06:22
Third complexity : Posterior Probability (Bays Theorem)
07:33
  • Along with jack knife approach,
  • Explanations of each portion of output
  • Excel check of linear discriminant function
Preview 12:13
  • Visualization of LDA operations
  • Understand the chart details and chart statistics
Demo of LDA using R part 02
08:24
LDA vs PCA side by side
03:41
  • Data visualization
  • Model development
  • Understand each component of the output of LDA
Demo of LDA for more than two classes - part 01
10:49

Model validation on train data set and test dataset

Demo of LDA for more than two classes - part 02
09:26
Industrial usage of LDA
04:06
Handling Special Cases (biased sample / differential misclassification) in LDA
06:42
Revisit your learning of LDA
5 questions
Check again
Revisit your learning of LDA - 02
4 questions
Closing Note
01:30
Requirements
  • Basic understanding of R and R studio
  • Basic understanding of statistics as the course will assume knowledge of linear regression, variance etc.
  • Basic fmiliarity with udemy platform - user should know how to download files etc
Description

This course has two parts. In part 1 Association rules (Market Basket Analysis) is explained. In Part 2, Linear Discriminant Analysis (LDA) is explained. L

--------------------------------------------------

Details of Part 1 - Association Rules / Market Basket Analysis (MBA)

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  • What is Market Basket Analysis (MBA) or Association rules
  • Usage of Association Rules - How it can be applied in a variety of situations 
  • How does an association rule look like?
  • Strength of an association rule - 
    1. Support measure
    2. Confidence measure 
    3. Lift measure
  • Basic Algorithm to derive rules
  • Demo of Basic Algorithm to derive rules - discussion on breadth first algorithm and depth first algorithm
  • Demo Using R - two examples
  • Assignment to fortify concepts

--------------------------------------------------

Details of Part 2 - Linear  (Market Basket Analysis)

----------------------------------------------------

  • Need of a classification model
  • Purpose of Linear Discriminant
  • A use case for classification
  • Formal definition of LDA
  • Analytics techniques applicability
  • Two usage of LDA 
    1. LDA for Variable Selection
    2. Demo of using LDA for Variable Selection
    3. Second usage of LDA - LDA for classification
  • Details on second practical usage of LDA
    1. Understand which are three important component to understand LDA properly
    2. First complexity of LDA - measure distance :Euclidean distance 
    3. First complexity of LDA - measure distance enhanced  :Mahalanobis distance
    4. Second complexity of LDA - Linear Discriminant function
    5. Third complexity of LDA - posterior probability / Bays theorem
  • Demo of LDA using R
    1. Along with jack knife approach
    2. Deep dive into LDA outputn
    3. Visualization of LDA operations
    4. Understand the LDA chart statistics
  • LDA vs PCA side by side
  • Demo of LDA for more than two classes: understand
    1. Data visualization
    2. Model development
    3. Model validation on train data set and test data sets
  • Industry usage of classification algorithm
  • Handling Special Cases in LDA
Who this course is for:
  • Market Research Professionals
  • Business Analytics professionals
  • Data Scientists