# Market Basket Analysis & Linear Discriminant Analysis with R

Master: Association rules (MBA) & it’s usage, Linear Discriminant Analysis (LDA) for classification & variable selection

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

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

• Part 1 – Association Rules (Market Basket Analysis) –> 9 lectures • 38min.
• Part 1- Association rules demo & quiz –> 5 lectures • 28min.
• Part 2 – Linear Discriminant Analysis (LDA) –> 8 lectures • 52min.
• Part 2 : Second practical usage of LDA – LDA for classification –> 14 lectures • 1hr 27min.

Requirements

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
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