Market Basket Analysis & Linear Discriminant Analysis with R - Udemy

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 - 
• Support measure
• Confidence measure 
• 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 
• LDA for Variable Selection
• Demo of using LDA for Variable Selection
• Second usage of LDA - LDA for classification

• Details on second practical usage of LDA
• Understand which are three important component to understand LDA properly
• First complexity of LDA - measure distance :Euclidean distance 
• First complexity of LDA - measure distance enhanced  :Mahalanobis distance
• Second complexity of LDA - Linear Discriminant function
• Third complexity of LDA - posterior probability / Bays theorem

• Demo of LDA using R
• Along with jack knife approach
• Deep dive into LDA outputn
• Visualization of LDA operations
• Understand the LDA chart statistics

• LDA vs PCA side by side
• Demo of LDA for more than two classes: understand
• Data visualization
• Model development
• Model validation on train data set and test data sets
• Industry usage of classification algorithm
• Handling Special Cases in LDA