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Data Mining - Business Case Analysis Assignment

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Added on  2020-03-23

Data Mining - Business Case Analysis Assignment

   Added on 2020-03-23

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DATA MININGBUSINESS CASE ANALYSISSTUDENT ID
Data Mining - Business Case Analysis Assignment_1
Question 1a)Output of PCA using XLMinerInterpretationTaking the total variance that ought to be explained as 80%, only the principal componentsnamed 1, 2, 3 and 4 would be considered while the remaining would be considered noise.Based on PCM (Principal Control Matrix), the key features for these shortlisted principalcomponents based on the respective magnitude of the Eigen values are identified below.PC1 – Key features – X1 and X2 (Aspect of performance: Financial)PC2 – Key features – X4 and X8 (Aspect of performance: Operational)PC3 – Key features – X3 and X7 (Aspect of performance: Financial)PC4 – Key features – X1 and X3 (Aspect of performance: Financial)1
Data Mining - Business Case Analysis Assignment_2
Data normalisation need – NoReason: Scale distortion does not seems significant which is captured from the respectivecontribution in the total variance for the PCA. Presence of significant difference in scales wouldlead to a much skewed result than has been obtained. Hence, PCA can be obtained withoutnormalising the data on utilities given for this exercise.(b) Advantages and disadvantages of using the principal component analysis (PCA) are givenbelow:Advantages – The structure of the given set of variable is easily visualized with the help ofPCA. This is a variable dimension reduction technique which reduces highly complex variablesinto simpler set of variable called as principal component. It reduces the noise in the variable setwhich provide more accurate result. The complexity of the data would be reduced in PCA andtherefore, the complexity of visualizing the data is also minimized. This technique is morecommon in “Criminal investigation.” The newly developed variables (principal components)would show zero correlation with the original (initial) variables. Therefore, it is easy todifferentiate the variance change and reduction among the variables. Disadvantages This technique is appropriate only for set of variables that show linear relations.Further, PCA analysis of dimension reduction is difficult to interpret. Also, when the data showsdiscrimination in terms of variances distribution, then data normalization needs to be done beforeapplying PCA in order to mitigate the risk that would arise due to the scale change. Theproduced “covariance matrix” is difficult to analyze. At times, when the training data is takenfrom blind source, the simple invariance also cannot be evaluated in PCA. PCA is also2
Data Mining - Business Case Analysis Assignment_3

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