Market Basket Analysis & Linear Discriminant Analysis with R | Data Science Online Course by Udemy

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

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Market Basket Analysis & Linear Discriminant Analysis with R

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)-What is Market Basket Analysis (MBA) or Association rulesUsage of Association Rules – How it can be applied in a variety of situationsHow does an association rule look like?Strength of an association rule -Support measureConfidence measureLift measureBasic Algorithm to derive rulesDemo of Basic Algorithm to derive rules – discussion on breadth first algorithm and depth first algorithmDemo Using R – two examplesAssignment to fortify concepts -Details of Part 2 – Linear (Market Basket Analysis)-Need of a classification modelPurpose of Linear DiscriminantA use case for classificationFormal definition of LDAAnalytics techniques applicability Two usage of LDALDA for Variable Selection Demo of using LDA for Variable Selection Second usage of LDA – LDA for classificationDetails on second practical usage of LDAUnderstand which are three important component to understand LDA properlyFirst complexity of LDA – measure distance: Euclidean distanceFirst complexity of LDA – measure distance enhanced: Mahalanobis distanceSecond complexity of LDA – Linear Discriminant functionThird complexity of LDA – posterior probability / Bays theoremDemo of LDA using RAlong with jack knife approachDeep dive into LDA outputnVisualization of LDA operationsUnderstand the LDA chart statistics LDA vs PCA side by sideDemo of LDA for more than two classes: understandData visualizationModel developmentModel validation on train data set and test data setsIndustry usage of classification algorithm Handling Special Cases in LDA

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