Market Basket Analysis & Linear Discriminant Analysis with R
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# Market Basket Analysis & Linear Discriminant Analysis with R

Master: Association rules (MBA) & it's usage, Linear Discriminant Analysis (LDA) for classification & variable selection
5.0 (1 rating)
3 students enrolled
Last updated 8/2017
English
Price: \$20
30-Day Money-Back Guarantee
Includes:
• 3.5 hours on-demand video
• 17 Supplemental Resources
• Access on mobile and TV
• Assignments
• Certificate of Completion
What Will I 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
View Curriculum
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

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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

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Details of Part 2 - Linear  (Market Basket Analysis)

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• 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 is the target audience?
• Market Research Professionals
• Data Scientists
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Curriculum For This Course
36 Lectures
03:24:28
+
Part 1 - Association Rules (Market Basket Analysis)
9 Lectures 37:45
Preview 01:10

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

Preview 04:03

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
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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

02:01

10 questions

Assignment solution
03:11
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Part 2 - Linear Discriminant Analysis (LDA)
8 Lectures 51:54
Preview 02:21

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
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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