Data Mining ASSIGNMENT- Evaluation and interpretation of the PCA

Added on - 12 Nov 2019

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DATA MININGASSIGNMENTSTUDENT ID[Pick the date]
Data MiningQuestion 1Dimension ReductionPart (A)Principal Component Analysis (PCA)Number of variables = 8XLMiner output is shown below:Evaluation and interpretation of the PCABased on the XLMiner output of variance analysis, it can be seen that 95% of variance isdescribed by the initial six principal components. Therefore, it is essential to reduce the principalcomponent matrix only for six components.1
Data MiningThe major six principal components and their respective significant features is finishedbelow:In order to find the most critical features among the six components, these are defined based ontheir contribution in variances. The critical features would be x2, x6 and x7.When any particular variable is indicating the maximum or higher level of variances among theother variables, then it is critical to normalize the data.The level of variances would be determined based on the variance analysis output fromXLMiner.2
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