Market Basket Analysis & Linear Discriminant Analysis with R
5.0 (1 rating)
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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)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
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
  • Full lifetime access
  • 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
  • Business Analytics 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

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
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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

Try your learning - assignment
02:01

Revisit your learning
10 questions

Assignment solution
03:11
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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
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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

Revisit your learning of LDA
5 questions

Check again
Revisit your learning of LDA - 02
4 questions

Closing Note
01:30
About the Instructor
Gopal Prasad Malakar
4.3 Average rating
1,602 Reviews
20,209 Students
16 Courses
Credit Card Analytics Professional - Trains on Data Mining

I am a seasoned Analytics professional with 16+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.