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Assignment on Data Mining Business Case Analysis

   

Added on  2020-04-07

7 Pages724 Words42 Views
Data MiningBusiness Case Analysis, Assignment -IIStudent Id/Name[Pick the date]Question 1
Assignment on Data Mining Business Case Analysis_1
Data MiningPART A The PCA of the utilities data is competed using XLMiner Analytical Tool and is shown below:Interpretation of PCA result From variance percent table shown above, it would be fair to say that nearly 80% ofvariance is discussed by the initial four principal components. Therefore, the main focus ofPCA would be based on these four principal components. The 1st principal component would be x1 and x2 which shows the “utility financial stage.”The 2nd principal component would be x4 and x8 which shows the “utility operationalperformance.”The 3rd principal component would be x3 and x7 which shows the “utility production cost ofelectricity.” 1
Assignment on Data Mining Business Case Analysis_2
Data MiningThe 4th principal component would be x1 and x3 which shows the “utility fixed cost ofelectricity.” Requirement of Normalization This is an essential step done prior to principal component analysis only when the data variancesare not magnified by the scale advantage that one or more variables may enjoy over others. Thismeans when few of the variables capture the total variance contribution owing to their advantageof scale then in order to ensure that effect of the scale is nullified, PCA data normalization isrequired. For the given data set, each of the variable’s contribution in the total magnitude ofvariance is significant and hence, it “normalization of data is not required.” PART B Advantages of PCA: Most appropriate technique when the PCA is run for the variables whichare associated with each other through linear relations. The PCA technique provides well-definedm or p dimensional space for the distribution of principal components cloud point which exhibitsseparate axis for each of the principal component. This helps to reduce the risk of over-fitting ofcomponents especially when there is presence of large sized data points.Disadvantages of PCA: This technique fails to analyze the dataset in which the data variablesare associated with each other through non-linear relations. Further, this technique cannot be ofuse when the dataset contains any categorical variable. Further, it can directly provide themagnitude of the principal components but it is difficult to understand the exact direction of2
Assignment on Data Mining Business Case Analysis_3

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